In [7]:
 
In [1]:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns

from DataProcessingMethods.DataPrepMultiClassv1 import prepare_data_pipeline

# Load the Forest Cover Type dataset
dataset_name = "Forest Cover Type Dataset"
df = pd.read_csv("Datasets/covtype.csv", na_values="?")  # Adjust path if needed

# Display basic information about the dataset
print(f"\n{dataset_name} shape: {df.shape}")
print(f"Number of classes in Cover_Type: {df['Cover_Type'].nunique()}")
print(f"Class distribution:\n{df['Cover_Type'].value_counts()}")
print("\nSample data:")
print(df.head())

# Define features for analysis
# Specify only the continuous numeric features for outlier detection
numeric_features = [
    'Elevation', 'Aspect', 'Slope', 
    'Horizontal_Distance_To_Hydrology', 'Vertical_Distance_To_Hydrology', 
    'Horizontal_Distance_To_Roadways', 
    'Hillshade_9am', 'Hillshade_Noon', 'Hillshade_3pm', 
    'Horizontal_Distance_To_Fire_Points'
]

# Features to display distributions for
dist_features = [
    'Slope', 'Horizontal_Distance_To_Hydrology', 'Vertical_Distance_To_Hydrology', 
    'Horizontal_Distance_To_Roadways', 'Hillshade_9am', 'Hillshade_Noon', 'Hillshade_3pm', 
    'Horizontal_Distance_To_Fire_Points'
]

# Target column
target = "Cover_Type"

# Since you don't want outlier removal for binary features (0/1),
# only include continuous features in the outlier detection
outlier_features = [
    'Elevation', 'Aspect', 'Slope', 
    'Horizontal_Distance_To_Hydrology', 'Vertical_Distance_To_Hydrology', 
    'Horizontal_Distance_To_Roadways', 
    'Hillshade_9am', 'Hillshade_Noon', 'Hillshade_3pm', 
    'Horizontal_Distance_To_Fire_Points'
]

# Run the data preparation pipeline
print("\nRunning data preparation pipeline...")
#df_no_outliers = prepare_data_pipeline(
#    df=df,
#    list_features_specialise_outliers=outlier_features,  # Only continuous features for outlier detection
#    numeric_features=numeric_features,
#    dist_features=dist_features,
#    target=target
#)

# Print summary of results
#print("\nPrepared dataset shape:", df_no_outliers.shape)
#print("\nClass distribution after preparation:")
#print(df_no_outliers[target].value_counts())

print("\nDone! Check for generated visualizations.")
Forest Cover Type Dataset shape: (581012, 55)
Number of classes in Cover_Type: 7
Class distribution:
Cover_Type
2    283301
1    211840
3     35754
7     20510
6     17367
5      9493
4      2747
Name: count, dtype: int64

Sample data:
   Elevation  Aspect  Slope  Horizontal_Distance_To_Hydrology  \
0       2596      51      3                               258   
1       2590      56      2                               212   
2       2804     139      9                               268   
3       2785     155     18                               242   
4       2595      45      2                               153   

   Vertical_Distance_To_Hydrology  Horizontal_Distance_To_Roadways  \
0                               0                              510   
1                              -6                              390   
2                              65                             3180   
3                             118                             3090   
4                              -1                              391   

   Hillshade_9am  Hillshade_Noon  Hillshade_3pm  \
0            221             232            148   
1            220             235            151   
2            234             238            135   
3            238             238            122   
4            220             234            150   

   Horizontal_Distance_To_Fire_Points  ...  Soil_Type32  Soil_Type33  \
0                                6279  ...            0            0   
1                                6225  ...            0            0   
2                                6121  ...            0            0   
3                                6211  ...            0            0   
4                                6172  ...            0            0   

   Soil_Type34  Soil_Type35  Soil_Type36  Soil_Type37  Soil_Type38  \
0            0            0            0            0            0   
1            0            0            0            0            0   
2            0            0            0            0            0   
3            0            0            0            0            0   
4            0            0            0            0            0   

   Soil_Type39  Soil_Type40  Cover_Type  
0            0            0           5  
1            0            0           5  
2            0            0           2  
3            0            0           2  
4            0            0           5  

[5 rows x 55 columns]

Running data preparation pipeline...

Done! Check for generated visualizations.
In [6]:
df_no_outliers.to_csv("Datasets/covtypeCLEANED.csv", index=False)
print("Done")
Done
In [2]:
from GenerationMethods.MultiClassification.MultiVAE2 import augment_dataframe_vae_enhanced
df_no_outliers = pd.read_csv("Datasets/covtypeCLEANED.csv")
# Run the augmentation
original_train, augmented_train, test_set, success = augment_dataframe_vae_enhanced(
    df=df_no_outliers,
    target='Cover_Type',
    test_size=0.25,
    random_state=42, 
    n_classes_to_augment=4, 
    ratio_limit=0.5,
    diminishing_factor=0.65,
    vae_epochs=2,             
    vae_batch_size=64,         
    latent_dim=48,
    hidden_dims=[512, 256, 128],
    temperature=0.8,
    matching_factor=0.25,
    early_stopping_patience=50  
)


if success:
    # CORRECT USAGE - This gets numeric features directly from the returned DataFrame
    numeric_features = augmented_train.select_dtypes(include=['number']).columns
    
    # Exclude the target and synthetic columns if they exist and are numeric
    columns_to_exclude = []
    if 'quality' in numeric_features:
        columns_to_exclude.append('quality')
    if 'synthetic' in numeric_features:
        columns_to_exclude.append('synthetic')
        
    # Filter numeric features to exclude certain columns
    if columns_to_exclude:
        numeric_features = [col for col in numeric_features if col not in columns_to_exclude]
    
    # Round numeric features
    
    augmented_train[numeric_features] = augmented_train[numeric_features].round(2)
    
    # Save outputs
    original_train.to_csv("OutputTrainingSets/original_trainVAEForestFINAL.csv", index=False)
    augmented_train.to_csv("OutputTrainingSets/augmented_trainVAEForestFINAL.csv", index=False)
    test_set.to_csv("OutputTrainingSets/test_setVAEForestFINAL.csv", index=False)
else:
    print("Augmentation failed. Check the error messages.")
2025-04-14 14:39:43.462946: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:479] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
2025-04-14 14:39:43.489860: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:10575] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
2025-04-14 14:39:43.489913: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1442] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
2025-04-14 14:39:43.507414: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
2025-04-14 14:39:44.597447: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
Starting enhanced VAE-based augmentation...
Target values range: [1, 7], unique classes: 7
Warning: max_label 7 >= num_classes 7, adjusting num_classes to 8
Training data shape: (427285, 54)
Test data shape: (142429, 54)
Training enhanced VAE model...
Using 8 classes for one-hot encoding (max label: 7)
2025-04-14 14:39:48.398834: E external/local_xla/xla/stream_executor/cuda/cuda_driver.cc:282] failed call to cuInit: CUDA_ERROR_NO_DEVICE: no CUDA-capable device is detected
Epoch 1/2
5675/5675 ━━━━━━━━━━━━━━━━━━━━ 103s 17ms/step - loss: 0.2908 - val_loss: 0.1185
Epoch 2/2
5675/5675 ━━━━━━━━━━━━━━━━━━━━ 95s 17ms/step - loss: 0.1187 - val_loss: 0.1100
Restoring model weights from the end of the best epoch: 2.
Could not load best model weights, using final weights
Class distribution: {1: 155577, 2: 208214, 3: 26803, 4: 2060, 5: 6808, 6: 13003, 7: 14820}
Classes to augment (from smallest to largest): [4, 5, 6, 7]
Class 4: Generating 1030 synthetic samples
  - Current count: 2060
  - Diminishing factor: 1.00
  - Theoretical target count: 208214
  - Limited by ratio constraint: max 1030 new samples
  - Final target count: 3090
65/65 ━━━━━━━━━━━━━━━━━━━━ 0s 4ms/step
33/33 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step
Class 5: Generating 3404 synthetic samples
  - Current count: 6808
  - Diminishing factor: 0.65
  - Theoretical target count: 135339
  - Limited by ratio constraint: max 3404 new samples
  - Final target count: 10212
213/213 ━━━━━━━━━━━━━━━━━━━━ 1s 3ms/step
107/107 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step
Class 6: Generating 6501 synthetic samples
  - Current count: 13003
  - Diminishing factor: 0.42
  - Theoretical target count: 87970
  - Limited by ratio constraint: max 6501 new samples
  - Final target count: 19504
407/407 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step
204/204 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step
Class 7: Generating 7410 synthetic samples
  - Current count: 14820
  - Diminishing factor: 0.27
  - Theoretical target count: 57180
  - Limited by ratio constraint: max 7410 new samples
  - Final target count: 22230
464/464 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step
232/232 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step
Debug - Synthetic samples shape before inverse transform: (18345, 54)
Debug - Synthetic samples range: [2.799956519083935e-06, 0.9997535943984985]
Applying quantile matching to synthetic samples...
Original DataFrame info:
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 427285 entries, 0 to 427284
Data columns (total 56 columns):
 #   Column                              Non-Null Count   Dtype  
---  ------                              --------------   -----  
 0   Elevation                           427285 non-null  float64
 1   Aspect                              427285 non-null  float64
 2   Slope                               427285 non-null  float64
 3   Horizontal_Distance_To_Hydrology    427285 non-null  float64
 4   Vertical_Distance_To_Hydrology      427285 non-null  float64
 5   Horizontal_Distance_To_Roadways     427285 non-null  float64
 6   Hillshade_9am                       427285 non-null  float64
 7   Hillshade_Noon                      427285 non-null  float64
 8   Hillshade_3pm                       427285 non-null  float64
 9   Horizontal_Distance_To_Fire_Points  427285 non-null  float64
 10  Wilderness_Area1                    427285 non-null  float64
 11  Wilderness_Area2                    427285 non-null  float64
 12  Wilderness_Area3                    427285 non-null  float64
 13  Wilderness_Area4                    427285 non-null  float64
 14  Soil_Type1                          427285 non-null  float64
 15  Soil_Type2                          427285 non-null  float64
 16  Soil_Type3                          427285 non-null  float64
 17  Soil_Type4                          427285 non-null  float64
 18  Soil_Type5                          427285 non-null  float64
 19  Soil_Type6                          427285 non-null  float64
 20  Soil_Type7                          427285 non-null  float64
 21  Soil_Type8                          427285 non-null  float64
 22  Soil_Type9                          427285 non-null  float64
 23  Soil_Type10                         427285 non-null  float64
 24  Soil_Type11                         427285 non-null  float64
 25  Soil_Type12                         427285 non-null  float64
 26  Soil_Type13                         427285 non-null  float64
 27  Soil_Type14                         427285 non-null  float64
 28  Soil_Type15                         427285 non-null  float64
 29  Soil_Type16                         427285 non-null  float64
 30  Soil_Type17                         427285 non-null  float64
 31  Soil_Type18                         427285 non-null  float64
 32  Soil_Type19                         427285 non-null  float64
 33  Soil_Type20                         427285 non-null  float64
 34  Soil_Type21                         427285 non-null  float64
 35  Soil_Type22                         427285 non-null  float64
 36  Soil_Type23                         427285 non-null  float64
 37  Soil_Type24                         427285 non-null  float64
 38  Soil_Type25                         427285 non-null  float64
 39  Soil_Type26                         427285 non-null  float64
 40  Soil_Type27                         427285 non-null  float64
 41  Soil_Type28                         427285 non-null  float64
 42  Soil_Type29                         427285 non-null  float64
 43  Soil_Type30                         427285 non-null  float64
 44  Soil_Type31                         427285 non-null  float64
 45  Soil_Type32                         427285 non-null  float64
 46  Soil_Type33                         427285 non-null  float64
 47  Soil_Type34                         427285 non-null  float64
 48  Soil_Type35                         427285 non-null  float64
 49  Soil_Type36                         427285 non-null  float64
 50  Soil_Type37                         427285 non-null  float64
 51  Soil_Type38                         427285 non-null  float64
 52  Soil_Type39                         427285 non-null  float64
 53  Soil_Type40                         427285 non-null  float64
 54  Cover_Type                          427285 non-null  int64  
 55  synthetic                           427285 non-null  bool   
dtypes: bool(1), float64(54), int64(1)
memory usage: 179.7 MB
None

Synthetic DataFrame info:
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 18345 entries, 0 to 18344
Data columns (total 56 columns):
 #   Column                              Non-Null Count  Dtype  
---  ------                              --------------  -----  
 0   Elevation                           18345 non-null  float64
 1   Aspect                              18345 non-null  float64
 2   Slope                               18345 non-null  float64
 3   Horizontal_Distance_To_Hydrology    18345 non-null  float64
 4   Vertical_Distance_To_Hydrology      18345 non-null  float64
 5   Horizontal_Distance_To_Roadways     18345 non-null  float64
 6   Hillshade_9am                       18345 non-null  float64
 7   Hillshade_Noon                      18345 non-null  float64
 8   Hillshade_3pm                       18345 non-null  float64
 9   Horizontal_Distance_To_Fire_Points  18345 non-null  float64
 10  Wilderness_Area1                    18345 non-null  float64
 11  Wilderness_Area2                    18345 non-null  float64
 12  Wilderness_Area3                    18345 non-null  float64
 13  Wilderness_Area4                    18345 non-null  float64
 14  Soil_Type1                          18345 non-null  float64
 15  Soil_Type2                          18345 non-null  float64
 16  Soil_Type3                          18345 non-null  float64
 17  Soil_Type4                          18345 non-null  float64
 18  Soil_Type5                          18345 non-null  float64
 19  Soil_Type6                          18345 non-null  float64
 20  Soil_Type7                          18345 non-null  float64
 21  Soil_Type8                          18345 non-null  float64
 22  Soil_Type9                          18345 non-null  float64
 23  Soil_Type10                         18345 non-null  float64
 24  Soil_Type11                         18345 non-null  float64
 25  Soil_Type12                         18345 non-null  float64
 26  Soil_Type13                         18345 non-null  float64
 27  Soil_Type14                         18345 non-null  float64
 28  Soil_Type15                         18345 non-null  float64
 29  Soil_Type16                         18345 non-null  float64
 30  Soil_Type17                         18345 non-null  float64
 31  Soil_Type18                         18345 non-null  float64
 32  Soil_Type19                         18345 non-null  float64
 33  Soil_Type20                         18345 non-null  float64
 34  Soil_Type21                         18345 non-null  float64
 35  Soil_Type22                         18345 non-null  float64
 36  Soil_Type23                         18345 non-null  float64
 37  Soil_Type24                         18345 non-null  float64
 38  Soil_Type25                         18345 non-null  float64
 39  Soil_Type26                         18345 non-null  float64
 40  Soil_Type27                         18345 non-null  float64
 41  Soil_Type28                         18345 non-null  float64
 42  Soil_Type29                         18345 non-null  float64
 43  Soil_Type30                         18345 non-null  float64
 44  Soil_Type31                         18345 non-null  float64
 45  Soil_Type32                         18345 non-null  float64
 46  Soil_Type33                         18345 non-null  float64
 47  Soil_Type34                         18345 non-null  float64
 48  Soil_Type35                         18345 non-null  float64
 49  Soil_Type36                         18345 non-null  float64
 50  Soil_Type37                         18345 non-null  float64
 51  Soil_Type38                         18345 non-null  float64
 52  Soil_Type39                         18345 non-null  float64
 53  Soil_Type40                         18345 non-null  float64
 54  Cover_Type                          18345 non-null  int64  
 55  synthetic                           18345 non-null  bool   
dtypes: bool(1), float64(54), int64(1)
memory usage: 7.7 MB
None

Augmented DataFrame info:
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 445630 entries, 0 to 445629
Data columns (total 56 columns):
 #   Column                              Non-Null Count   Dtype  
---  ------                              --------------   -----  
 0   Elevation                           445630 non-null  float64
 1   Aspect                              445630 non-null  float64
 2   Slope                               445630 non-null  float64
 3   Horizontal_Distance_To_Hydrology    445630 non-null  float64
 4   Vertical_Distance_To_Hydrology      445630 non-null  float64
 5   Horizontal_Distance_To_Roadways     445630 non-null  float64
 6   Hillshade_9am                       445630 non-null  float64
 7   Hillshade_Noon                      445630 non-null  float64
 8   Hillshade_3pm                       445630 non-null  float64
 9   Horizontal_Distance_To_Fire_Points  445630 non-null  float64
 10  Wilderness_Area1                    445630 non-null  float64
 11  Wilderness_Area2                    445630 non-null  float64
 12  Wilderness_Area3                    445630 non-null  float64
 13  Wilderness_Area4                    445630 non-null  float64
 14  Soil_Type1                          445630 non-null  float64
 15  Soil_Type2                          445630 non-null  float64
 16  Soil_Type3                          445630 non-null  float64
 17  Soil_Type4                          445630 non-null  float64
 18  Soil_Type5                          445630 non-null  float64
 19  Soil_Type6                          445630 non-null  float64
 20  Soil_Type7                          445630 non-null  float64
 21  Soil_Type8                          445630 non-null  float64
 22  Soil_Type9                          445630 non-null  float64
 23  Soil_Type10                         445630 non-null  float64
 24  Soil_Type11                         445630 non-null  float64
 25  Soil_Type12                         445630 non-null  float64
 26  Soil_Type13                         445630 non-null  float64
 27  Soil_Type14                         445630 non-null  float64
 28  Soil_Type15                         445630 non-null  float64
 29  Soil_Type16                         445630 non-null  float64
 30  Soil_Type17                         445630 non-null  float64
 31  Soil_Type18                         445630 non-null  float64
 32  Soil_Type19                         445630 non-null  float64
 33  Soil_Type20                         445630 non-null  float64
 34  Soil_Type21                         445630 non-null  float64
 35  Soil_Type22                         445630 non-null  float64
 36  Soil_Type23                         445630 non-null  float64
 37  Soil_Type24                         445630 non-null  float64
 38  Soil_Type25                         445630 non-null  float64
 39  Soil_Type26                         445630 non-null  float64
 40  Soil_Type27                         445630 non-null  float64
 41  Soil_Type28                         445630 non-null  float64
 42  Soil_Type29                         445630 non-null  float64
 43  Soil_Type30                         445630 non-null  float64
 44  Soil_Type31                         445630 non-null  float64
 45  Soil_Type32                         445630 non-null  float64
 46  Soil_Type33                         445630 non-null  float64
 47  Soil_Type34                         445630 non-null  float64
 48  Soil_Type35                         445630 non-null  float64
 49  Soil_Type36                         445630 non-null  float64
 50  Soil_Type37                         445630 non-null  float64
 51  Soil_Type38                         445630 non-null  float64
 52  Soil_Type39                         445630 non-null  float64
 53  Soil_Type40                         445630 non-null  float64
 54  Cover_Type                          445630 non-null  int64  
 55  synthetic                           445630 non-null  bool   
dtypes: bool(1), float64(54), int64(1)
memory usage: 187.4 MB
None
Augmented data summary:
  - Original samples: 427285
  - Synthetic samples: 18345
  - Total samples: 445630
Class distribution before: {1: 155577, 2: 208214, 3: 26803, 4: 2060, 5: 6808, 6: 13003, 7: 14820}
Class distribution after: {1: 155577, 2: 208214, 3: 26803, 4: 3090, 5: 10212, 6: 19504, 7: 22230}
Saved results to ./output/
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 189ms/step
<Figure size 1000x600 with 0 Axes>
In [3]:
from ValidationMethods.MultiClassValidation import validate_synthetic_data_per_class, analyze_target_distribution
from GenerationMethods.MultiClassification.undersampleMajorityClasses import undersample_majority_classes
import pandas as pd

# Load the CSV files generated by the augmentation process.
# (These files are assumed to have been generated using an oversampling method adapted for multi-class data.)
original_train2 = pd.read_csv("OutputTrainingSets/original_trainVAEForestFINAL.csv")
augmented_train2 = pd.read_csv("OutputTrainingSets/augmented_trainVAEForestFINAL.csv")
test_set2 = pd.read_csv("OutputTrainingSets/test_setVAEForestFINAL.csv")


# Apply undersampling before augmentation
df_augmented_train2 = undersample_majority_classes(augmented_train2, 
                                              majority_classes=[1, 2], 
                                              target_ratio=0.15)
augmented_train2.to_csv("OutputTrainingSets/augmented_trainVAEForestFINAL.csv", index=False)

#print(flag)
#augmented_train2[numeric_features] = augmented_train2[numeric_features].round(2)

# Define the columns to keep: continuous features + target.
continuous_features = ['Elevation', 'Aspect', 'Slope', 'Horizontal_Distance_To_Hydrology', 'Vertical_Distance_To_Hydrology', 'Horizontal_Distance_To_Roadways', 'Hillshade_9am', 'Hillshade_Noon', 'Hillshade_3pm', 'Horizontal_Distance_To_Fire_Points', 'Wilderness_Area1', 'Wilderness_Area2', 'Wilderness_Area3', 'Wilderness_Area4', 'Soil_Type1', 'Soil_Type2', 'Soil_Type3', 'Soil_Type4', 'Soil_Type5', 'Soil_Type6', 'Soil_Type7', 'Soil_Type8', 'Soil_Type9', 'Soil_Type10', 'Soil_Type11', 'Soil_Type12', 'Soil_Type13', 'Soil_Type14', 'Soil_Type15', 'Soil_Type16', 'Soil_Type17', 'Soil_Type18', 'Soil_Type19', 'Soil_Type20', 'Soil_Type21', 'Soil_Type22', 'Soil_Type23', 'Soil_Type24', 'Soil_Type25', 'Soil_Type26', 'Soil_Type27', 'Soil_Type28', 'Soil_Type29', 'Soil_Type30', 'Soil_Type31', 'Soil_Type32', 'Soil_Type33', 'Soil_Type34', 'Soil_Type35', 'Soil_Type36', 'Soil_Type37', 'Soil_Type38', 'Soil_Type39', 'Soil_Type40']
categorical_features = ["Cover_Type"]
cols_to_keep = continuous_features + categorical_features

# Keep only the desired columns and drop rows with missing values.
original_train2 = original_train2[cols_to_keep].dropna()
# For the augmented training set, also keep the "synthetic" column.
augmented_train2 = augmented_train2[cols_to_keep + ['synthetic']].dropna()
test_set2 = test_set2[cols_to_keep].dropna()
    
target = "Cover_Type"

# Extract synthetic samples from the augmented training set (synthetic == True).
synthetic_samples = augmented_train2[augmented_train2['synthetic'] == True]

# Run the validation analysis comparing original training data against synthetic samples.
metrics = validate_synthetic_data_per_class(
    original=original_train2,
    synthetic=synthetic_samples,
    continuous_features=continuous_features,
    categorical_features=categorical_features,
    target = target,
    num_classes = 4,
    distance_threshold=0.5,
    density_threshold=0.5,
    gamma=1.0,
    plot=True
)
    
print("Validation metrics:")
print(metrics)
    
print("\n### Target Distribution Analysis on Original Training Set ###")
analyze_target_distribution(original_train2, target=target)
print("\n### Target Distribution Analysis on Augmented Training Set ###")
analyze_target_distribution(augmented_train2, target=target)
print("\n### Target Distribution Analysis on Test Set ###")
analyze_target_distribution(test_set2, target=target)

### Validation for class: 4 ###

### Continuous Features Validation ###

Feature: Elevation
Original: mean=2224.681, std=101.540
Synthetic: mean=2552.035, std=244.547
KS test: statistic=0.700, p-value=0.000
-> Significant difference detected!
Synthetic shape after dropna: (1030, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    1030.000000
mean     2552.034553
std       244.546754
min      1869.240000
25%      2372.165000
50%      2587.895000
75%      2747.302500
max      3021.000000
Name: Elevation, dtype: float64
No description has been provided for this image
Feature: Aspect
Original: mean=137.600, std=87.461
Synthetic: mean=115.970, std=88.486
KS test: statistic=0.211, p-value=0.000
-> Significant difference detected!
Synthetic shape after dropna: (1030, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    1030.000000
mean      115.969981
std        88.485571
min         0.000000
25%        45.610000
50%        94.175000
75%       165.000000
max       358.000000
Name: Aspect, dtype: float64
No description has been provided for this image
Feature: Slope
Original: mean=18.610, std=9.280
Synthetic: mean=17.368, std=6.877
KS test: statistic=0.140, p-value=0.000
-> Significant difference detected!
Synthetic shape after dropna: (1030, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    1030.000000
mean       17.367835
std         6.876850
min         2.000000
25%        12.000000
50%        16.305000
75%        22.000000
max        50.000000
Name: Slope, dtype: float64
No description has been provided for this image
Feature: Horizontal_Distance_To_Hydrology
Original: mean=105.259, std=139.052
Synthetic: mean=94.667, std=94.635
KS test: statistic=0.231, p-value=0.000
-> Significant difference detected!
Synthetic shape after dropna: (1030, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    1030.000000
mean       94.667136
std        94.634996
min         0.000000
25%        30.000000
50%        67.000000
75%       150.000000
max       621.890000
Name: Horizontal_Distance_To_Hydrology, dtype: float64
No description has been provided for this image
Feature: Vertical_Distance_To_Hydrology
Original: mean=40.335, std=58.823
Synthetic: mean=22.769, std=36.872
KS test: statistic=0.202, p-value=0.000
-> Significant difference detected!
Synthetic shape after dropna: (1030, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    1030.000000
mean       22.769379
std        36.872228
min       -87.450000
25%         0.262500
50%        13.000000
75%        37.000000
max       242.100000
Name: Vertical_Distance_To_Hydrology, dtype: float64
No description has been provided for this image
Feature: Horizontal_Distance_To_Roadways
Original: mean=920.270, std=368.143
Synthetic: mean=1128.825, std=817.765
KS test: statistic=0.226, p-value=0.000
-> Significant difference detected!
Synthetic shape after dropna: (1030, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    1030.000000
mean     1128.825136
std       817.765398
min        25.160000
25%       551.455000
50%       962.455000
75%      1483.800000
max      6056.680000
Name: Horizontal_Distance_To_Roadways, dtype: float64
No description has been provided for this image
Feature: Hillshade_9am
Original: mean=228.063, std=24.382
Synthetic: mean=224.608, std=17.487
KS test: statistic=0.244, p-value=0.000
-> Significant difference detected!
Synthetic shape after dropna: (1030, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    1030.000000
mean      224.607864
std        17.487470
min       157.000000
25%       215.000000
50%       227.000000
75%       237.000000
max       254.000000
Name: Hillshade_9am, dtype: float64
No description has been provided for this image
Feature: Hillshade_Noon
Original: mean=216.899, std=20.883
Synthetic: mean=220.407, std=19.620
KS test: statistic=0.093, p-value=0.000
-> Significant difference detected!
Synthetic shape after dropna: (1030, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    1030.000000
mean      220.407398
std        19.620388
min        98.450000
25%       210.000000
50%       223.000000
75%       234.000000
max       254.000000
Name: Hillshade_Noon, dtype: float64
No description has been provided for this image
Feature: Hillshade_3pm
Original: mean=111.642, std=49.051
Synthetic: mean=115.813, std=35.778
KS test: statistic=0.147, p-value=0.000
-> Significant difference detected!
Synthetic shape after dropna: (1030, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    1030.00000
mean      115.81266
std        35.77751
min         0.00000
25%        96.00000
50%       117.70500
75%       139.00000
max       213.04000
Name: Hillshade_3pm, dtype: float64
No description has been provided for this image
Feature: Horizontal_Distance_To_Fire_Points
Original: mean=860.698, std=480.851
Synthetic: mean=1193.648, std=773.064
KS test: statistic=0.172, p-value=0.000
-> Significant difference detected!
Synthetic shape after dropna: (1030, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    1030.000000
mean     1193.648291
std       773.064062
min        55.550000
25%       633.000000
50%      1036.760000
75%      1599.892500
max      5673.380000
Name: Horizontal_Distance_To_Fire_Points, dtype: float64
No description has been provided for this image
Feature: Wilderness_Area1
Original: mean=0.000, std=0.000
Synthetic: mean=0.171, std=0.377
KS test: statistic=0.171, p-value=0.000
-> Significant difference detected!
Synthetic shape after dropna: (1030, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    1030.000000
mean        0.170874
std         0.376581
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max         1.000000
Name: Wilderness_Area1, dtype: float64
No description has been provided for this image
Feature: Wilderness_Area2
Original: mean=0.000, std=0.000
Synthetic: mean=0.013, std=0.113
KS test: statistic=0.014, p-value=1.000
-> No significant difference in distribution
Synthetic shape after dropna: (1030, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    1030.000000
mean        0.013165
std         0.112982
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max         1.000000
Name: Wilderness_Area2, dtype: float64
No description has been provided for this image
Feature: Wilderness_Area3
Original: mean=0.000, std=0.000
Synthetic: mean=0.027, std=0.163
KS test: statistic=0.027, p-value=0.687
-> No significant difference in distribution
Synthetic shape after dropna: (1030, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    1030.000000
mean        0.027184
std         0.162700
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max         1.000000
Name: Wilderness_Area3, dtype: float64
No description has been provided for this image
Feature: Wilderness_Area4
Original: mean=1.000, std=0.000
Synthetic: mean=0.525, std=0.499
KS test: statistic=0.477, p-value=0.000
-> Significant difference detected!
Synthetic shape after dropna: (1030, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    1030.000000
mean        0.524961
std         0.498779
min         0.000000
25%         0.000000
50%         1.000000
75%         1.000000
max         1.000000
Name: Wilderness_Area4, dtype: float64
No description has been provided for this image
Feature: Soil_Type1
Original: mean=0.066, std=0.248
Synthetic: mean=0.037, std=0.187
KS test: statistic=0.033, p-value=0.459
-> No significant difference in distribution
Synthetic shape after dropna: (1030, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    1030.000000
mean        0.036816
std         0.186635
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max         1.000000
Name: Soil_Type1, dtype: float64
No description has been provided for this image
Feature: Soil_Type2
Original: mean=0.045, std=0.207
Synthetic: mean=0.018, std=0.130
KS test: statistic=0.028, p-value=0.644
-> No significant difference in distribution
Synthetic shape after dropna: (1030, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    1030.000000
mean        0.017573
std         0.129705
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max         1.000000
Name: Soil_Type2, dtype: float64
No description has been provided for this image
Feature: Soil_Type3
Original: mean=0.367, std=0.482
Synthetic: mean=0.114, std=0.316
KS test: statistic=0.257, p-value=0.000
-> Significant difference detected!
Synthetic shape after dropna: (1030, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    1030.000000
mean        0.114408
std         0.315871
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max         1.000000
Name: Soil_Type3, dtype: float64
No description has been provided for this image
Feature: Soil_Type4
Original: mean=0.062, std=0.241
Synthetic: mean=0.038, std=0.191
KS test: statistic=0.025, p-value=0.771
-> No significant difference in distribution
Synthetic shape after dropna: (1030, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    1030.000000
mean        0.038427
std         0.191483
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max         1.000000
Name: Soil_Type4, dtype: float64
No description has been provided for this image
Feature: Soil_Type5
Original: mean=0.019, std=0.136
Synthetic: mean=0.015, std=0.116
KS test: statistic=0.006, p-value=1.000
-> No significant difference in distribution
Synthetic shape after dropna: (1030, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    1030.000000
mean        0.014825
std         0.115999
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max         1.000000
Name: Soil_Type5, dtype: float64
No description has been provided for this image
Feature: Soil_Type6
Original: mean=0.114, std=0.318
Synthetic: mean=0.023, std=0.147
KS test: statistic=0.093, p-value=0.000
-> Significant difference detected!
Synthetic shape after dropna: (1030, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    1030.000000
mean        0.022612
std         0.147351
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max         1.000000
Name: Soil_Type6, dtype: float64
No description has been provided for this image
Feature: Soil_Type7
Original: mean=0.000, std=0.000
Synthetic: mean=0.002, std=0.038
KS test: statistic=0.003, p-value=1.000
-> No significant difference in distribution
Synthetic shape after dropna: (1030, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    1030.000000
mean        0.001913
std         0.037798
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max         0.950000
Name: Soil_Type7, dtype: float64
No description has been provided for this image
Feature: Soil_Type8
Original: mean=0.000, std=0.000
Synthetic: mean=0.002, std=0.037
KS test: statistic=0.002, p-value=1.000
-> No significant difference in distribution
Synthetic shape after dropna: (1030, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    1030.000000
mean        0.001621
std         0.037489
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max         1.000000
Name: Soil_Type8, dtype: float64
No description has been provided for this image
Feature: Soil_Type9
Original: mean=0.000, std=0.000
Synthetic: mean=0.012, std=0.106
KS test: statistic=0.014, p-value=1.000
-> No significant difference in distribution
Synthetic shape after dropna: (1030, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    1030.000000
mean        0.011699
std         0.106010
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max         1.000000
Name: Soil_Type9, dtype: float64
No description has been provided for this image
Feature: Soil_Type10
Original: mean=0.080, std=0.272
Synthetic: mean=0.006, std=0.076
KS test: statistic=0.074, p-value=0.001
-> Significant difference detected!
Synthetic shape after dropna: (1030, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    1030.000000
mean        0.005825
std         0.076138
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max         1.000000
Name: Soil_Type10, dtype: float64
No description has been provided for this image
Feature: Soil_Type11
Original: mean=0.013, std=0.112
Synthetic: mean=0.019, std=0.137
KS test: statistic=0.007, p-value=1.000
-> No significant difference in distribution
Synthetic shape after dropna: (1030, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    1030.000000
mean        0.019291
std         0.137214
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max         1.000000
Name: Soil_Type11, dtype: float64
No description has been provided for this image
Feature: Soil_Type12
Original: mean=0.000, std=0.000
Synthetic: mean=0.040, std=0.194
KS test: statistic=0.042, p-value=0.181
-> No significant difference in distribution
Synthetic shape after dropna: (1030, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    1030.000000
mean        0.039874
std         0.194399
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max         1.000000
Name: Soil_Type12, dtype: float64
No description has been provided for this image
Feature: Soil_Type13
Original: mean=0.000, std=0.000
Synthetic: mean=0.007, std=0.082
KS test: statistic=0.007, p-value=1.000
-> No significant difference in distribution
Synthetic shape after dropna: (1030, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    1030.000000
mean        0.006796
std         0.082198
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max         1.000000
Name: Soil_Type13, dtype: float64
No description has been provided for this image
Feature: Soil_Type14
Original: mean=0.059, std=0.236
Synthetic: mean=0.002, std=0.044
KS test: statistic=0.057, p-value=0.022
-> Significant difference detected!
Synthetic shape after dropna: (1030, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    1030.000000
mean        0.001942
std         0.044044
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max         1.000000
Name: Soil_Type14, dtype: float64
No description has been provided for this image
Feature: Soil_Type15
Original: mean=0.000, std=0.000
Synthetic: mean=0.000, std=0.000
KS test: statistic=0.000, p-value=1.000
-> No significant difference in distribution
Synthetic shape after dropna: (1030, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    1030.0
mean        0.0
std         0.0
min         0.0
25%         0.0
50%         0.0
75%         0.0
max         0.0
Name: Soil_Type15, dtype: float64
No description has been provided for this image
Feature: Soil_Type16
Original: mean=0.019, std=0.136
Synthetic: mean=0.015, std=0.119
KS test: statistic=0.005, p-value=1.000
-> No significant difference in distribution
Synthetic shape after dropna: (1030, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    1030.000000
mean        0.015039
std         0.119479
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max         1.000000
Name: Soil_Type16, dtype: float64
No description has been provided for this image
Feature: Soil_Type17
Original: mean=0.156, std=0.363
Synthetic: mean=0.041, std=0.197
KS test: statistic=0.117, p-value=0.000
-> Significant difference detected!
Synthetic shape after dropna: (1030, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    1030.000000
mean        0.040835
std         0.196956
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max         1.000000
Name: Soil_Type17, dtype: float64
No description has been provided for this image
Feature: Soil_Type18
Original: mean=0.000, std=0.000
Synthetic: mean=0.004, std=0.062
KS test: statistic=0.006, p-value=1.000
-> No significant difference in distribution
Synthetic shape after dropna: (1030, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    1030.000000
mean        0.004476
std         0.062211
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max         1.000000
Name: Soil_Type18, dtype: float64
No description has been provided for this image
Feature: Soil_Type19
Original: mean=0.000, std=0.000
Synthetic: mean=0.034, std=0.181
KS test: statistic=0.035, p-value=0.368
-> No significant difference in distribution
Synthetic shape after dropna: (1030, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    1030.000000
mean        0.034272
std         0.181381
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max         1.000000
Name: Soil_Type19, dtype: float64
No description has been provided for this image
Feature: Soil_Type20
Original: mean=0.000, std=0.000
Synthetic: mean=0.016, std=0.125
KS test: statistic=0.017, p-value=0.992
-> No significant difference in distribution
Synthetic shape after dropna: (1030, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    1030.000000
mean        0.016019
std         0.124640
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max         1.000000
Name: Soil_Type20, dtype: float64
No description has been provided for this image
Feature: Soil_Type21
Original: mean=0.000, std=0.000
Synthetic: mean=0.002, std=0.038
KS test: statistic=0.004, p-value=1.000
-> No significant difference in distribution
Synthetic shape after dropna: (1030, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    1030.000000
mean        0.002087
std         0.038124
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max         1.000000
Name: Soil_Type21, dtype: float64
No description has been provided for this image
Feature: Soil_Type22
Original: mean=0.000, std=0.000
Synthetic: mean=0.031, std=0.174
KS test: statistic=0.031, p-value=0.518
-> No significant difference in distribution
Synthetic shape after dropna: (1030, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    1030.000000
mean        0.031068
std         0.173586
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max         1.000000
Name: Soil_Type22, dtype: float64
No description has been provided for this image
Feature: Soil_Type23
Original: mean=0.000, std=0.000
Synthetic: mean=0.023, std=0.151
KS test: statistic=0.023, p-value=0.847
-> No significant difference in distribution
Synthetic shape after dropna: (1030, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    1030.000000
mean        0.023301
std         0.150931
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max         1.000000
Name: Soil_Type23, dtype: float64
No description has been provided for this image
Feature: Soil_Type24
Original: mean=0.000, std=0.000
Synthetic: mean=0.020, std=0.140
KS test: statistic=0.020, p-value=0.936
-> No significant difference in distribution
Synthetic shape after dropna: (1030, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    1030.000000
mean        0.020078
std         0.139579
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max         1.000000
Name: Soil_Type24, dtype: float64
No description has been provided for this image
Feature: Soil_Type25
Original: mean=0.000, std=0.000
Synthetic: mean=0.000, std=0.009
KS test: statistic=0.001, p-value=1.000
-> No significant difference in distribution
Synthetic shape after dropna: (1030, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    1030.000000
mean        0.000282
std         0.009036
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max         0.290000
Name: Soil_Type25, dtype: float64
No description has been provided for this image
Feature: Soil_Type26
Original: mean=0.000, std=0.000
Synthetic: mean=0.000, std=0.000
KS test: statistic=0.000, p-value=1.000
-> No significant difference in distribution
Synthetic shape after dropna: (1030, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    1030.0
mean        0.0
std         0.0
min         0.0
25%         0.0
50%         0.0
75%         0.0
max         0.0
Name: Soil_Type26, dtype: float64
No description has been provided for this image
Feature: Soil_Type27
Original: mean=0.000, std=0.000
Synthetic: mean=0.000, std=0.000
KS test: statistic=0.000, p-value=1.000
-> No significant difference in distribution
Synthetic shape after dropna: (1030, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    1030.0
mean        0.0
std         0.0
min         0.0
25%         0.0
50%         0.0
75%         0.0
max         0.0
Name: Soil_Type27, dtype: float64
No description has been provided for this image
Feature: Soil_Type28
Original: mean=0.000, std=0.000
Synthetic: mean=0.001, std=0.031
KS test: statistic=0.001, p-value=1.000
-> No significant difference in distribution
Synthetic shape after dropna: (1030, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    1030.000000
mean        0.000971
std         0.031159
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max         1.000000
Name: Soil_Type28, dtype: float64
No description has been provided for this image
Feature: Soil_Type29
Original: mean=0.000, std=0.000
Synthetic: mean=0.086, std=0.281
KS test: statistic=0.086, p-value=0.000
-> Significant difference detected!
Synthetic shape after dropna: (1030, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    1030.000000
mean        0.086408
std         0.281102
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max         1.000000
Name: Soil_Type29, dtype: float64
No description has been provided for this image
Feature: Soil_Type30
Original: mean=0.000, std=0.000
Synthetic: mean=0.041, std=0.198
KS test: statistic=0.042, p-value=0.181
-> No significant difference in distribution
Synthetic shape after dropna: (1030, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    1030.000000
mean        0.040874
std         0.197873
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max         1.000000
Name: Soil_Type30, dtype: float64
No description has been provided for this image
Feature: Soil_Type31
Original: mean=0.000, std=0.000
Synthetic: mean=0.036, std=0.185
KS test: statistic=0.037, p-value=0.305
-> No significant difference in distribution
Synthetic shape after dropna: (1030, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    1030.000000
mean        0.035845
std         0.184695
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max         1.000000
Name: Soil_Type31, dtype: float64
No description has been provided for this image
Feature: Soil_Type32
Original: mean=0.000, std=0.000
Synthetic: mean=0.067, std=0.250
KS test: statistic=0.067, p-value=0.004
-> Significant difference detected!
Synthetic shape after dropna: (1030, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    1030.000000
mean        0.066990
std         0.250127
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max         1.000000
Name: Soil_Type32, dtype: float64
No description has been provided for this image
Feature: Soil_Type33
Original: mean=0.000, std=0.000
Synthetic: mean=0.039, std=0.193
KS test: statistic=0.040, p-value=0.225
-> No significant difference in distribution
Synthetic shape after dropna: (1030, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    1030.000000
mean        0.039117
std         0.193450
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max         1.000000
Name: Soil_Type33, dtype: float64
No description has been provided for this image
Feature: Soil_Type34
Original: mean=0.000, std=0.000
Synthetic: mean=0.002, std=0.036
KS test: statistic=0.002, p-value=1.000
-> No significant difference in distribution
Synthetic shape after dropna: (1030, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    1030.000000
mean        0.001505
std         0.035546
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max         1.000000
Name: Soil_Type34, dtype: float64
No description has been provided for this image
Feature: Soil_Type35
Original: mean=0.000, std=0.000
Synthetic: mean=0.002, std=0.044
KS test: statistic=0.002, p-value=1.000
-> No significant difference in distribution
Synthetic shape after dropna: (1030, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    1030.000000
mean        0.001942
std         0.044044
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max         1.000000
Name: Soil_Type35, dtype: float64
No description has been provided for this image
Feature: Soil_Type36
Original: mean=0.000, std=0.000
Synthetic: mean=0.000, std=0.007
KS test: statistic=0.001, p-value=1.000
-> No significant difference in distribution
Synthetic shape after dropna: (1030, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    1030.000000
mean        0.000233
std         0.007478
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max         0.240000
Name: Soil_Type36, dtype: float64
No description has been provided for this image
Feature: Soil_Type37
Original: mean=0.000, std=0.000
Synthetic: mean=0.001, std=0.024
KS test: statistic=0.001, p-value=1.000
-> No significant difference in distribution
Synthetic shape after dropna: (1030, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    1030.000000
mean        0.000757
std         0.024304
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max         0.780000
Name: Soil_Type37, dtype: float64
No description has been provided for this image
Feature: Soil_Type38
Original: mean=0.000, std=0.000
Synthetic: mean=0.000, std=0.000
KS test: statistic=0.000, p-value=1.000
-> No significant difference in distribution
Synthetic shape after dropna: (1030, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    1030.0
mean        0.0
std         0.0
min         0.0
25%         0.0
50%         0.0
75%         0.0
max         0.0
Name: Soil_Type38, dtype: float64
No description has been provided for this image
Feature: Soil_Type39
Original: mean=0.000, std=0.000
Synthetic: mean=0.000, std=0.000
KS test: statistic=0.000, p-value=1.000
-> No significant difference in distribution
Synthetic shape after dropna: (1030, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    1030.0
mean        0.0
std         0.0
min         0.0
25%         0.0
50%         0.0
75%         0.0
max         0.0
Name: Soil_Type39, dtype: float64
No description has been provided for this image
Feature: Soil_Type40
Original: mean=0.000, std=0.000
Synthetic: mean=0.000, std=0.000
KS test: statistic=0.000, p-value=1.000
-> No significant difference in distribution
Synthetic shape after dropna: (1030, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    1030.0
mean        0.0
std         0.0
min         0.0
25%         0.0
50%         0.0
75%         0.0
max         0.0
Name: Soil_Type40, dtype: float64
No description has been provided for this image
### Categorical Features Validation ###

Feature: Cover_Type
Original counts:
 Cover_Type
4    2060
Name: count, dtype: int64
Synthetic counts:
 Cover_Type
4    1030
Name: count, dtype: int64
Chi-squared test: statistic=0.000, p-value=1.000
-> No significant difference in categorical distribution
No description has been provided for this image
### Coverage Metric ###
Coverage: 0.00% of original samples have a synthetic neighbor within 0.5

### Diversity Metric ###
Average pairwise distance among synthetic samples: 1412.639
Standard deviation of pairwise distances: 834.938

### Density Metric ###
Average local density: 0.000 neighbors within a radius of 0.5

### Discriminative Score ###
Discriminative score (classifier accuracy): 0.950

### MMD Metric ###
Maximum Mean Discrepancy (MMD): 0.001


### Validation for class: 5 ###

### Continuous Features Validation ###

Feature: Elevation
Original: mean=2793.034, std=89.394
Synthetic: mean=2965.944, std=113.905
KS test: statistic=0.698, p-value=0.000
-> Significant difference detected!
Synthetic shape after dropna: (3404, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    3404.000000
mean     2965.944007
std       113.905480
min      1979.440000
25%      2922.030000
50%      2983.000000
75%      3030.722500
max      3336.900000
Name: Elevation, dtype: float64
No description has been provided for this image
Feature: Aspect
Original: mean=141.199, std=92.274
Synthetic: mean=122.287, std=103.146
KS test: statistic=0.207, p-value=0.000
-> Significant difference detected!
Synthetic shape after dropna: (3404, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    3404.000000
mean      122.287065
std       103.146078
min         0.000000
25%        39.000000
50%        90.000000
75%       187.772500
max       359.840000
Name: Aspect, dtype: float64
No description has been provided for this image
Feature: Slope
Original: mean=16.794, std=8.052
Synthetic: mean=13.449, std=6.613
KS test: statistic=0.234, p-value=0.000
-> Significant difference detected!
Synthetic shape after dropna: (3404, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    3404.000000
mean       13.448681
std         6.612809
min         0.000000
25%         9.000000
50%        13.000000
75%        17.000000
max        37.000000
Name: Slope, dtype: float64
No description has been provided for this image
Feature: Horizontal_Distance_To_Hydrology
Original: mean=209.276, std=171.615
Synthetic: mean=217.462, std=167.377
KS test: statistic=0.066, p-value=0.000
-> Significant difference detected!
Synthetic shape after dropna: (3404, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    3404.000000
mean      217.462465
std       167.376878
min         0.000000
25%        85.000000
50%       190.000000
75%       308.100000
max      1260.830000
Name: Horizontal_Distance_To_Hydrology, dtype: float64
No description has been provided for this image
Feature: Vertical_Distance_To_Hydrology
Original: mean=50.432, std=57.814
Synthetic: mean=29.069, std=44.324
KS test: statistic=0.191, p-value=0.000
-> Significant difference detected!
Synthetic shape after dropna: (3404, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    3404.000000
mean       29.069489
std        44.324080
min      -135.950000
25%         1.000000
50%        17.320000
75%        47.000000
max       252.630000
Name: Vertical_Distance_To_Hydrology, dtype: float64
No description has been provided for this image
Feature: Horizontal_Distance_To_Roadways
Original: mean=1338.482, std=1016.511
Synthetic: mean=1917.998, std=1205.615
KS test: statistic=0.217, p-value=0.000
-> Significant difference detected!
Synthetic shape after dropna: (3404, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    3404.000000
mean     1917.998452
std      1205.615344
min        13.720000
25%      1020.000000
50%      1661.270000
75%      2608.040000
max      6612.270000
Name: Horizontal_Distance_To_Roadways, dtype: float64
No description has been provided for this image
Feature: Hillshade_9am
Original: mean=223.398, std=22.901
Synthetic: mean=223.966, std=18.259
KS test: statistic=0.103, p-value=0.000
-> Significant difference detected!
Synthetic shape after dropna: (3404, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    3404.000000
mean      223.966272
std        18.258650
min       123.670000
25%       214.000000
50%       227.000000
75%       237.000000
max       254.000000
Name: Hillshade_9am, dtype: float64
No description has been provided for this image
Feature: Hillshade_Noon
Original: mean=219.019, std=24.695
Synthetic: mean=222.570, std=19.923
KS test: statistic=0.093, p-value=0.000
-> Significant difference detected!
Synthetic shape after dropna: (3404, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    3404.000000
mean      222.569562
std        19.923300
min       105.800000
25%       212.000000
50%       225.000000
75%       236.000000
max       254.000000
Name: Hillshade_Noon, dtype: float64
No description has been provided for this image
Feature: Hillshade_3pm
Original: mean=121.848, std=49.567
Synthetic: mean=124.726, std=37.812
KS test: statistic=0.110, p-value=0.000
-> Significant difference detected!
Synthetic shape after dropna: (3404, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    3404.000000
mean      124.725790
std        37.812444
min         0.000000
25%       103.000000
50%       127.000000
75%       149.000000
max       234.980000
Name: Hillshade_3pm, dtype: float64
No description has been provided for this image
Feature: Horizontal_Distance_To_Fire_Points
Original: mean=1440.032, std=644.540
Synthetic: mean=2014.503, std=1277.630
KS test: statistic=0.243, p-value=0.000
-> Significant difference detected!
Synthetic shape after dropna: (3404, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    3404.000000
mean     2014.503308
std      1277.629502
min        16.340000
25%      1091.160000
50%      1772.885000
75%      2566.285000
max      7000.480000
Name: Horizontal_Distance_To_Fire_Points, dtype: float64
No description has been provided for this image
Feature: Wilderness_Area1
Original: mean=0.375, std=0.484
Synthetic: mean=0.654, std=0.476
KS test: statistic=0.279, p-value=0.000
-> Significant difference detected!
Synthetic shape after dropna: (3404, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    3404.000000
mean        0.653790
std         0.475754
min         0.000000
25%         0.000000
50%         1.000000
75%         1.000000
max         1.000000
Name: Wilderness_Area1, dtype: float64
No description has been provided for this image
Feature: Wilderness_Area2
Original: mean=0.000, std=0.000
Synthetic: mean=0.015, std=0.121
KS test: statistic=0.015, p-value=0.663
-> No significant difference in distribution
Synthetic shape after dropna: (3404, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    3404.000000
mean        0.014938
std         0.120732
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max         1.000000
Name: Wilderness_Area2, dtype: float64
No description has been provided for this image
Feature: Wilderness_Area3
Original: mean=0.625, std=0.484
Synthetic: mean=0.502, std=0.500
KS test: statistic=0.123, p-value=0.000
-> Significant difference detected!
Synthetic shape after dropna: (3404, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    3404.000000
mean        0.502394
std         0.499831
min         0.000000
25%         0.000000
50%         1.000000
75%         1.000000
max         1.000000
Name: Wilderness_Area3, dtype: float64
No description has been provided for this image
Feature: Wilderness_Area4
Original: mean=0.000, std=0.000
Synthetic: mean=0.000, std=0.017
KS test: statistic=0.000, p-value=1.000
-> No significant difference in distribution
Synthetic shape after dropna: (3404, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    3404.000000
mean        0.000294
std         0.017140
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max         1.000000
Name: Wilderness_Area4, dtype: float64
No description has been provided for this image
Feature: Soil_Type1
Original: mean=0.000, std=0.000
Synthetic: mean=0.003, std=0.051
KS test: statistic=0.003, p-value=1.000
-> No significant difference in distribution
Synthetic shape after dropna: (3404, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    3404.000000
mean        0.002644
std         0.051359
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max         1.000000
Name: Soil_Type1, dtype: float64
No description has been provided for this image
Feature: Soil_Type2
Original: mean=0.030, std=0.169
Synthetic: mean=0.008, std=0.087
KS test: statistic=0.022, p-value=0.226
-> No significant difference in distribution
Synthetic shape after dropna: (3404, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    3404.000000
mean        0.007670
std         0.087092
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max         1.000000
Name: Soil_Type2, dtype: float64
No description has been provided for this image
Feature: Soil_Type3
Original: mean=0.000, std=0.000
Synthetic: mean=0.004, std=0.062
KS test: statistic=0.004, p-value=1.000
-> No significant difference in distribution
Synthetic shape after dropna: (3404, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    3404.000000
mean        0.003934
std         0.061887
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max         1.000000
Name: Soil_Type3, dtype: float64
No description has been provided for this image
Feature: Soil_Type4
Original: mean=0.065, std=0.246
Synthetic: mean=0.036, std=0.186
KS test: statistic=0.030, p-value=0.038
-> Significant difference detected!
Synthetic shape after dropna: (3404, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    3404.000000
mean        0.035796
std         0.185704
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max         1.000000
Name: Soil_Type4, dtype: float64
No description has been provided for this image
Feature: Soil_Type5
Original: mean=0.000, std=0.000
Synthetic: mean=0.000, std=0.004
KS test: statistic=0.001, p-value=1.000
-> No significant difference in distribution
Synthetic shape after dropna: (3404, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    3404.000000
mean        0.000082
std         0.003908
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max         0.220000
Name: Soil_Type5, dtype: float64
No description has been provided for this image
Feature: Soil_Type6
Original: mean=0.000, std=0.000
Synthetic: mean=0.006, std=0.079
KS test: statistic=0.007, p-value=1.000
-> No significant difference in distribution
Synthetic shape after dropna: (3404, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    3404.000000
mean        0.006407
std         0.078919
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max         1.000000
Name: Soil_Type6, dtype: float64
No description has been provided for this image
Feature: Soil_Type7
Original: mean=0.000, std=0.000
Synthetic: mean=0.000, std=0.014
KS test: statistic=0.000, p-value=1.000
-> No significant difference in distribution
Synthetic shape after dropna: (3404, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    3404.000000
mean        0.000247
std         0.014397
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max         0.840000
Name: Soil_Type7, dtype: float64
No description has been provided for this image
Feature: Soil_Type8
Original: mean=0.000, std=0.000
Synthetic: mean=0.000, std=0.015
KS test: statistic=0.001, p-value=1.000
-> No significant difference in distribution
Synthetic shape after dropna: (3404, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    3404.000000
mean        0.000370
std         0.015265
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max         0.730000
Name: Soil_Type8, dtype: float64
No description has been provided for this image
Feature: Soil_Type9
Original: mean=0.000, std=0.000
Synthetic: mean=0.004, std=0.062
KS test: statistic=0.005, p-value=1.000
-> No significant difference in distribution
Synthetic shape after dropna: (3404, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    3404.000000
mean        0.004230
std         0.061908
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max         1.000000
Name: Soil_Type9, dtype: float64
No description has been provided for this image
Feature: Soil_Type10
Original: mean=0.029, std=0.168
Synthetic: mean=0.002, std=0.042
KS test: statistic=0.027, p-value=0.065
-> No significant difference in distribution
Synthetic shape after dropna: (3404, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    3404.000000
mean        0.001763
std         0.041953
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max         1.000000
Name: Soil_Type10, dtype: float64
No description has been provided for this image
Feature: Soil_Type11
Original: mean=0.073, std=0.261
Synthetic: mean=0.031, std=0.173
KS test: statistic=0.043, p-value=0.000
-> Significant difference detected!
Synthetic shape after dropna: (3404, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    3404.000000
mean        0.031178
std         0.173346
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max         1.000000
Name: Soil_Type11, dtype: float64
No description has been provided for this image
Feature: Soil_Type12
Original: mean=0.000, std=0.000
Synthetic: mean=0.066, std=0.248
KS test: statistic=0.066, p-value=0.000
-> Significant difference detected!
Synthetic shape after dropna: (3404, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    3404.000000
mean        0.066014
std         0.247910
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max         1.000000
Name: Soil_Type12, dtype: float64
No description has been provided for this image
Feature: Soil_Type13
Original: mean=0.143, std=0.350
Synthetic: mean=0.088, std=0.283
KS test: statistic=0.055, p-value=0.000
-> Significant difference detected!
Synthetic shape after dropna: (3404, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    3404.000000
mean        0.088437
std         0.283293
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max         1.000000
Name: Soil_Type13, dtype: float64
No description has been provided for this image
Feature: Soil_Type14
Original: mean=0.000, std=0.000
Synthetic: mean=0.001, std=0.034
KS test: statistic=0.002, p-value=1.000
-> No significant difference in distribution
Synthetic shape after dropna: (3404, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    3404.000000
mean        0.001381
std         0.033655
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max         1.000000
Name: Soil_Type14, dtype: float64
No description has been provided for this image
Feature: Soil_Type15
Original: mean=0.000, std=0.000
Synthetic: mean=0.000, std=0.015
KS test: statistic=0.001, p-value=1.000
-> No significant difference in distribution
Synthetic shape after dropna: (3404, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    3404.000000
mean        0.000267
std         0.015258
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max         0.890000
Name: Soil_Type15, dtype: float64
No description has been provided for this image
Feature: Soil_Type16
Original: mean=0.002, std=0.042
Synthetic: mean=0.003, std=0.058
KS test: statistic=0.002, p-value=1.000
-> No significant difference in distribution
Synthetic shape after dropna: (3404, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    3404.000000
mean        0.003449
std         0.057585
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max         1.000000
Name: Soil_Type16, dtype: float64
No description has been provided for this image
Feature: Soil_Type17
Original: mean=0.065, std=0.246
Synthetic: mean=0.004, std=0.066
KS test: statistic=0.061, p-value=0.000
-> Significant difference detected!
Synthetic shape after dropna: (3404, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    3404.000000
mean        0.004424
std         0.065669
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max         1.000000
Name: Soil_Type17, dtype: float64
No description has been provided for this image
Feature: Soil_Type18
Original: mean=0.003, std=0.051
Synthetic: mean=0.005, std=0.070
KS test: statistic=0.003, p-value=1.000
-> No significant difference in distribution
Synthetic shape after dropna: (3404, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    3404.000000
mean        0.005179
std         0.069701
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max         1.000000
Name: Soil_Type18, dtype: float64
No description has been provided for this image
Feature: Soil_Type19
Original: mean=0.006, std=0.080
Synthetic: mean=0.005, std=0.071
KS test: statistic=0.001, p-value=1.000
-> No significant difference in distribution
Synthetic shape after dropna: (3404, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    3404.000000
mean        0.005167
std         0.071212
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max         1.000000
Name: Soil_Type19, dtype: float64
No description has been provided for this image
Feature: Soil_Type20
Original: mean=0.006, std=0.079
Synthetic: mean=0.016, std=0.125
KS test: statistic=0.010, p-value=0.973
-> No significant difference in distribution
Synthetic shape after dropna: (3404, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    3404.000000
mean        0.015943
std         0.124541
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max         1.000000
Name: Soil_Type20, dtype: float64
No description has been provided for this image
Feature: Soil_Type21
Original: mean=0.000, std=0.000
Synthetic: mean=0.001, std=0.031
KS test: statistic=0.001, p-value=1.000
-> No significant difference in distribution
Synthetic shape after dropna: (3404, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    3404.000000
mean        0.001061
std         0.031132
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max         1.000000
Name: Soil_Type21, dtype: float64
No description has been provided for this image
Feature: Soil_Type22
Original: mean=0.000, std=0.000
Synthetic: mean=0.048, std=0.214
KS test: statistic=0.049, p-value=0.000
-> Significant difference detected!
Synthetic shape after dropna: (3404, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    3404.000000
mean        0.048443
std         0.214296
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max         1.000000
Name: Soil_Type22, dtype: float64
No description has been provided for this image
Feature: Soil_Type23
Original: mean=0.079, std=0.270
Synthetic: mean=0.145, std=0.352
KS test: statistic=0.067, p-value=0.000
-> Significant difference detected!
Synthetic shape after dropna: (3404, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    3404.000000
mean        0.145082
std         0.351946
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max         1.000000
Name: Soil_Type23, dtype: float64
No description has been provided for this image
Feature: Soil_Type24
Original: mean=0.007, std=0.084
Synthetic: mean=0.034, std=0.180
KS test: statistic=0.028, p-value=0.062
-> No significant difference in distribution
Synthetic shape after dropna: (3404, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    3404.000000
mean        0.033922
std         0.180494
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max         1.000000
Name: Soil_Type24, dtype: float64
No description has been provided for this image
Feature: Soil_Type25
Original: mean=0.000, std=0.000
Synthetic: mean=0.001, std=0.023
KS test: statistic=0.001, p-value=1.000
-> No significant difference in distribution
Synthetic shape after dropna: (3404, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    3404.000000
mean        0.000679
std         0.022749
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max         0.890000
Name: Soil_Type25, dtype: float64
No description has been provided for this image
Feature: Soil_Type26
Original: mean=0.016, std=0.124
Synthetic: mean=0.004, std=0.060
KS test: statistic=0.012, p-value=0.879
-> No significant difference in distribution
Synthetic shape after dropna: (3404, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    3404.000000
mean        0.003784
std         0.060031
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max         1.000000
Name: Soil_Type26, dtype: float64
No description has been provided for this image
Feature: Soil_Type27
Original: mean=0.000, std=0.000
Synthetic: mean=0.001, std=0.029
KS test: statistic=0.001, p-value=1.000
-> No significant difference in distribution
Synthetic shape after dropna: (3404, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    3404.000000
mean        0.000958
std         0.028761
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max         1.000000
Name: Soil_Type27, dtype: float64
No description has been provided for this image
Feature: Soil_Type28
Original: mean=0.001, std=0.036
Synthetic: mean=0.001, std=0.025
KS test: statistic=0.001, p-value=1.000
-> No significant difference in distribution
Synthetic shape after dropna: (3404, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    3404.00000
mean        0.00074
std         0.02518
min         0.00000
25%         0.00000
50%         0.00000
75%         0.00000
max         1.00000
Name: Soil_Type28, dtype: float64
No description has been provided for this image
Feature: Soil_Type29
Original: mean=0.110, std=0.313
Synthetic: mean=0.258, std=0.437
KS test: statistic=0.149, p-value=0.000
-> Significant difference detected!
Synthetic shape after dropna: (3404, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    3404.000000
mean        0.258296
std         0.437439
min         0.000000
25%         0.000000
50%         0.000000
75%         1.000000
max         1.000000
Name: Soil_Type29, dtype: float64
No description has been provided for this image
Feature: Soil_Type30
Original: mean=0.224, std=0.417
Synthetic: mean=0.143, std=0.350
KS test: statistic=0.082, p-value=0.000
-> Significant difference detected!
Synthetic shape after dropna: (3404, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    3404.000000
mean        0.143431
std         0.350142
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max         1.000000
Name: Soil_Type30, dtype: float64
No description has been provided for this image
Feature: Soil_Type31
Original: mean=0.035, std=0.184
Synthetic: mean=0.047, std=0.211
KS test: statistic=0.012, p-value=0.896
-> No significant difference in distribution
Synthetic shape after dropna: (3404, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    3404.000000
mean        0.046701
std         0.210761
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max         1.000000
Name: Soil_Type31, dtype: float64
No description has been provided for this image
Feature: Soil_Type32
Original: mean=0.050, std=0.217
Synthetic: mean=0.076, std=0.265
KS test: statistic=0.027, p-value=0.078
-> No significant difference in distribution
Synthetic shape after dropna: (3404, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    3404.000000
mean        0.076322
std         0.265453
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max         1.000000
Name: Soil_Type32, dtype: float64
No description has been provided for this image
Feature: Soil_Type33
Original: mean=0.056, std=0.230
Synthetic: mean=0.090, std=0.287
KS test: statistic=0.035, p-value=0.008
-> Significant difference detected!
Synthetic shape after dropna: (3404, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    3404.000000
mean        0.090452
std         0.286748
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max         1.000000
Name: Soil_Type33, dtype: float64
No description has been provided for this image
Feature: Soil_Type34
Original: mean=0.000, std=0.000
Synthetic: mean=0.001, std=0.035
KS test: statistic=0.001, p-value=1.000
-> No significant difference in distribution
Synthetic shape after dropna: (3404, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    3404.000000
mean        0.001316
std         0.034831
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max         1.000000
Name: Soil_Type34, dtype: float64
No description has been provided for this image
Feature: Soil_Type35
Original: mean=0.000, std=0.000
Synthetic: mean=0.003, std=0.053
KS test: statistic=0.004, p-value=1.000
-> No significant difference in distribution
Synthetic shape after dropna: (3404, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    3404.000000
mean        0.003005
std         0.053173
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max         1.000000
Name: Soil_Type35, dtype: float64
No description has been provided for this image
Feature: Soil_Type36
Original: mean=0.000, std=0.000
Synthetic: mean=0.001, std=0.024
KS test: statistic=0.001, p-value=1.000
-> No significant difference in distribution
Synthetic shape after dropna: (3404, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    3404.000000
mean        0.000864
std         0.024387
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max         0.950000
Name: Soil_Type36, dtype: float64
No description has been provided for this image
Feature: Soil_Type37
Original: mean=0.000, std=0.000
Synthetic: mean=0.000, std=0.007
KS test: statistic=0.001, p-value=1.000
-> No significant difference in distribution
Synthetic shape after dropna: (3404, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    3404.000000
mean        0.000123
std         0.006864
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max         0.400000
Name: Soil_Type37, dtype: float64
No description has been provided for this image
Feature: Soil_Type38
Original: mean=0.000, std=0.000
Synthetic: mean=0.001, std=0.034
KS test: statistic=0.001, p-value=1.000
-> No significant difference in distribution
Synthetic shape after dropna: (3404, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    3404.000000
mean        0.001175
std         0.034264
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max         1.000000
Name: Soil_Type38, dtype: float64
No description has been provided for this image
Feature: Soil_Type39
Original: mean=0.000, std=0.000
Synthetic: mean=0.001, std=0.024
KS test: statistic=0.001, p-value=1.000
-> No significant difference in distribution
Synthetic shape after dropna: (3404, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    3404.000000
mean        0.000588
std         0.024236
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max         1.000000
Name: Soil_Type39, dtype: float64
No description has been provided for this image
Feature: Soil_Type40
Original: mean=0.000, std=0.000
Synthetic: mean=0.001, std=0.030
KS test: statistic=0.001, p-value=1.000
-> No significant difference in distribution
Synthetic shape after dropna: (3404, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    3404.000000
mean        0.000881
std         0.029678
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max         1.000000
Name: Soil_Type40, dtype: float64
No description has been provided for this image
### Categorical Features Validation ###

Feature: Cover_Type
Original counts:
 Cover_Type
5    6808
Name: count, dtype: int64
Synthetic counts:
 Cover_Type
5    3404
Name: count, dtype: int64
Chi-squared test: statistic=0.000, p-value=1.000
-> No significant difference in categorical distribution
No description has been provided for this image
### Coverage Metric ###
Coverage: 0.00% of original samples have a synthetic neighbor within 0.5

### Diversity Metric ###
Average pairwise distance among synthetic samples: 2169.976
Standard deviation of pairwise distances: 1254.788

### Density Metric ###
Average local density: 0.000 neighbors within a radius of 0.5

### Discriminative Score ###
Discriminative score (classifier accuracy): 0.844

### MMD Metric ###
Maximum Mean Discrepancy (MMD): 0.000


### Validation for class: 6 ###

### Continuous Features Validation ###

Feature: Elevation
Original: mean=2418.807, std=188.404
Synthetic: mean=2739.624, std=223.717
KS test: statistic=0.619, p-value=0.000
-> Significant difference detected!
Synthetic shape after dropna: (6501, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    6501.000000
mean     2739.624093
std       223.716760
min      1864.120000
25%      2625.690000
50%      2790.920000
75%      2909.230000
max      3206.790000
Name: Elevation, dtype: float64
No description has been provided for this image
Feature: Aspect
Original: mean=180.823, std=133.865
Synthetic: mean=189.044, std=113.115
KS test: statistic=0.166, p-value=0.000
-> Significant difference detected!
Synthetic shape after dropna: (6501, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    6501.000000
mean      189.044476
std       113.114935
min         0.000000
25%        86.090000
50%       179.530000
75%       305.200000
max       359.950000
Name: Aspect, dtype: float64
No description has been provided for this image
Feature: Slope
Original: mean=19.026, std=7.917
Synthetic: mean=17.133, std=7.451
KS test: statistic=0.147, p-value=0.000
-> Significant difference detected!
Synthetic shape after dropna: (6501, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    6501.000000
mean       17.132766
std         7.451402
min         1.000000
25%        12.000000
50%        16.000000
75%        22.000000
max        49.460000
Name: Slope, dtype: float64
No description has been provided for this image
Feature: Horizontal_Distance_To_Hydrology
Original: mean=158.618, std=123.966
Synthetic: mean=190.371, std=152.933
KS test: statistic=0.099, p-value=0.000
-> Significant difference detected!
Synthetic shape after dropna: (6501, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    6501.000000
mean      190.370848
std       152.933106
min         0.000000
25%        67.000000
50%       153.000000
75%       272.000000
max      1034.540000
Name: Horizontal_Distance_To_Hydrology, dtype: float64
No description has been provided for this image
Feature: Vertical_Distance_To_Hydrology
Original: mean=44.921, std=46.373
Synthetic: mean=44.005, std=49.462
KS test: statistic=0.038, p-value=0.000
-> Significant difference detected!
Synthetic shape after dropna: (6501, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    6501.000000
mean       44.004761
std        49.462488
min      -117.460000
25%         8.000000
50%        30.000000
75%        67.000000
max       383.000000
Name: Vertical_Distance_To_Hydrology, dtype: float64
No description has been provided for this image
Feature: Horizontal_Distance_To_Roadways
Original: mean=1033.641, std=570.311
Synthetic: mean=1553.178, std=1060.268
KS test: statistic=0.227, p-value=0.000
-> Significant difference detected!
Synthetic shape after dropna: (6501, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    6501.000000
mean     1553.177557
std      1060.268349
min         2.290000
25%       768.930000
50%      1315.170000
75%      2091.740000
max      6431.660000
Name: Horizontal_Distance_To_Roadways, dtype: float64
No description has been provided for this image
Feature: Hillshade_9am
Original: mean=192.932, std=33.425
Synthetic: mean=196.415, std=28.491
KS test: statistic=0.108, p-value=0.000
-> Significant difference detected!
Synthetic shape after dropna: (6501, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    6501.000000
mean      196.414545
std        28.490848
min        49.100000
25%       180.000000
50%       200.000000
75%       218.000000
max       254.000000
Name: Hillshade_9am, dtype: float64
No description has been provided for this image
Feature: Hillshade_Noon
Original: mean=209.804, std=24.278
Synthetic: mean=217.619, std=20.515
KS test: statistic=0.151, p-value=0.000
-> Significant difference detected!
Synthetic shape after dropna: (6501, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    6501.000000
mean      217.619365
std        20.514909
min       100.900000
25%       206.000000
50%       220.000000
75%       232.000000
max       254.000000
Name: Hillshade_Noon, dtype: float64
No description has been provided for this image
Feature: Hillshade_3pm
Original: mean=148.202, std=45.370
Synthetic: mean=154.924, std=34.725
KS test: statistic=0.131, p-value=0.000
-> Significant difference detected!
Synthetic shape after dropna: (6501, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    6501.000000
mean      154.924455
std        34.725021
min         0.000000
25%       132.000000
50%       154.000000
75%       179.000000
max       251.000000
Name: Hillshade_3pm, dtype: float64
No description has been provided for this image
Feature: Horizontal_Distance_To_Fire_Points
Original: mean=1058.292, std=579.888
Synthetic: mean=1533.564, std=993.259
KS test: statistic=0.255, p-value=0.000
-> Significant difference detected!
Synthetic shape after dropna: (6501, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    6501.000000
mean     1533.564327
std       993.259036
min         3.270000
25%       808.000000
50%      1357.300000
75%      2040.570000
max      6582.090000
Name: Horizontal_Distance_To_Fire_Points, dtype: float64
No description has been provided for this image
Feature: Wilderness_Area1
Original: mean=0.000, std=0.000
Synthetic: mean=0.215, std=0.410
KS test: statistic=0.215, p-value=0.000
-> Significant difference detected!
Synthetic shape after dropna: (6501, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    6501.000000
mean        0.214729
std         0.410434
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max         1.000000
Name: Wilderness_Area1, dtype: float64
No description has been provided for this image
Feature: Wilderness_Area2
Original: mean=0.000, std=0.000
Synthetic: mean=0.021, std=0.143
KS test: statistic=0.021, p-value=0.042
-> Significant difference detected!
Synthetic shape after dropna: (6501, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    6501.000000
mean        0.021031
std         0.143375
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max         1.000000
Name: Wilderness_Area2, dtype: float64
No description has been provided for this image
Feature: Wilderness_Area3
Original: mean=0.441, std=0.496
Synthetic: mean=0.350, std=0.477
KS test: statistic=0.091, p-value=0.000
-> Significant difference detected!
Synthetic shape after dropna: (6501, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    6501.000000
mean        0.349932
std         0.476798
min         0.000000
25%         0.000000
50%         0.000000
75%         1.000000
max         1.000000
Name: Wilderness_Area3, dtype: float64
No description has been provided for this image
Feature: Wilderness_Area4
Original: mean=0.559, std=0.496
Synthetic: mean=0.099, std=0.298
KS test: statistic=0.462, p-value=0.000
-> Significant difference detected!
Synthetic shape after dropna: (6501, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    6501.000000
mean        0.098942
std         0.298039
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max         1.000000
Name: Wilderness_Area4, dtype: float64
No description has been provided for this image
Feature: Soil_Type1
Original: mean=0.044, std=0.205
Synthetic: mean=0.007, std=0.082
KS test: statistic=0.038, p-value=0.000
-> Significant difference detected!
Synthetic shape after dropna: (6501, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    6501.000000
mean        0.007054
std         0.081681
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max         1.000000
Name: Soil_Type1, dtype: float64
No description has been provided for this image
Feature: Soil_Type2
Original: mean=0.074, std=0.262
Synthetic: mean=0.026, std=0.157
KS test: statistic=0.049, p-value=0.000
-> Significant difference detected!
Synthetic shape after dropna: (6501, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    6501.000000
mean        0.025721
std         0.157280
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max         1.000000
Name: Soil_Type2, dtype: float64
No description has been provided for this image
Feature: Soil_Type3
Original: mean=0.012, std=0.111
Synthetic: mean=0.003, std=0.054
KS test: statistic=0.010, p-value=0.814
-> No significant difference in distribution
Synthetic shape after dropna: (6501, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    6501.000000
mean        0.003000
std         0.054216
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max         1.000000
Name: Soil_Type3, dtype: float64
No description has been provided for this image
Feature: Soil_Type4
Original: mean=0.036, std=0.187
Synthetic: mean=0.025, std=0.157
KS test: statistic=0.011, p-value=0.624
-> No significant difference in distribution
Synthetic shape after dropna: (6501, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    6501.000000
mean        0.025296
std         0.156589
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max         1.000000
Name: Soil_Type4, dtype: float64
No description has been provided for this image
Feature: Soil_Type5
Original: mean=0.033, std=0.179
Synthetic: mean=0.004, std=0.060
KS test: statistic=0.030, p-value=0.001
-> Significant difference detected!
Synthetic shape after dropna: (6501, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    6501.000000
mean        0.003844
std         0.060414
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max         1.000000
Name: Soil_Type5, dtype: float64
No description has been provided for this image
Feature: Soil_Type6
Original: mean=0.076, std=0.266
Synthetic: mean=0.016, std=0.124
KS test: statistic=0.061, p-value=0.000
-> Significant difference detected!
Synthetic shape after dropna: (6501, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    6501.000000
mean        0.015713
std         0.123504
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max         1.000000
Name: Soil_Type6, dtype: float64
No description has been provided for this image
Feature: Soil_Type7
Original: mean=0.000, std=0.000
Synthetic: mean=0.001, std=0.023
KS test: statistic=0.002, p-value=1.000
-> No significant difference in distribution
Synthetic shape after dropna: (6501, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    6501.000000
mean        0.000792
std         0.023151
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max         1.000000
Name: Soil_Type7, dtype: float64
No description has been provided for this image
Feature: Soil_Type8
Original: mean=0.000, std=0.000
Synthetic: mean=0.001, std=0.022
KS test: statistic=0.001, p-value=1.000
-> No significant difference in distribution
Synthetic shape after dropna: (6501, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    6501.000000
mean        0.000661
std         0.021751
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max         0.950000
Name: Soil_Type8, dtype: float64
No description has been provided for this image
Feature: Soil_Type9
Original: mean=0.000, std=0.000
Synthetic: mean=0.002, std=0.048
KS test: statistic=0.003, p-value=1.000
-> No significant difference in distribution
Synthetic shape after dropna: (6501, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    6501.000000
mean        0.002430
std         0.048158
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max         1.000000
Name: Soil_Type9, dtype: float64
No description has been provided for this image
Feature: Soil_Type10
Original: mean=0.512, std=0.500
Synthetic: mean=0.158, std=0.364
KS test: statistic=0.356, p-value=0.000
-> Significant difference detected!
Synthetic shape after dropna: (6501, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    6501.000000
mean        0.157519
std         0.363670
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max         1.000000
Name: Soil_Type10, dtype: float64
No description has been provided for this image
Feature: Soil_Type11
Original: mean=0.030, std=0.171
Synthetic: mean=0.030, std=0.170
KS test: statistic=0.001, p-value=1.000
-> No significant difference in distribution
Synthetic shape after dropna: (6501, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    6501.000000
mean        0.029908
std         0.169660
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max         1.000000
Name: Soil_Type11, dtype: float64
No description has been provided for this image
Feature: Soil_Type12
Original: mean=0.000, std=0.000
Synthetic: mean=0.058, std=0.234
KS test: statistic=0.058, p-value=0.000
-> Significant difference detected!
Synthetic shape after dropna: (6501, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    6501.000000
mean        0.058080
std         0.233594
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max         1.000000
Name: Soil_Type12, dtype: float64
No description has been provided for this image
Feature: Soil_Type13
Original: mean=0.035, std=0.183
Synthetic: mean=0.019, std=0.134
KS test: statistic=0.017, p-value=0.173
-> No significant difference in distribution
Synthetic shape after dropna: (6501, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    6501.000000
mean        0.018573
std         0.134455
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max         1.000000
Name: Soil_Type13, dtype: float64
No description has been provided for this image
Feature: Soil_Type14
Original: mean=0.020, std=0.138
Synthetic: mean=0.002, std=0.044
KS test: statistic=0.018, p-value=0.131
-> No significant difference in distribution
Synthetic shape after dropna: (6501, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    6501.000000
mean        0.002046
std         0.044053
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max         1.000000
Name: Soil_Type14, dtype: float64
No description has been provided for this image
Feature: Soil_Type15
Original: mean=0.000, std=0.015
Synthetic: mean=0.001, std=0.023
KS test: statistic=0.001, p-value=1.000
-> No significant difference in distribution
Synthetic shape after dropna: (6501, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    6501.000000
mean        0.000846
std         0.023435
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max         1.000000
Name: Soil_Type15, dtype: float64
No description has been provided for this image
Feature: Soil_Type16
Original: mean=0.015, std=0.122
Synthetic: mean=0.006, std=0.079
KS test: statistic=0.009, p-value=0.844
-> No significant difference in distribution
Synthetic shape after dropna: (6501, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    6501.000000
mean        0.006488
std         0.078824
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max         1.000000
Name: Soil_Type16, dtype: float64
No description has been provided for this image
Feature: Soil_Type17
Original: mean=0.041, std=0.198
Synthetic: mean=0.007, std=0.079
KS test: statistic=0.035, p-value=0.000
-> Significant difference detected!
Synthetic shape after dropna: (6501, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    6501.000000
mean        0.006673
std         0.079209
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max         1.000000
Name: Soil_Type17, dtype: float64
No description has been provided for this image
Feature: Soil_Type18
Original: mean=0.000, std=0.000
Synthetic: mean=0.005, std=0.068
KS test: statistic=0.005, p-value=1.000
-> No significant difference in distribution
Synthetic shape after dropna: (6501, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    6501.000000
mean        0.004727
std         0.068065
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max         1.000000
Name: Soil_Type18, dtype: float64
No description has been provided for this image
Feature: Soil_Type19
Original: mean=0.000, std=0.000
Synthetic: mean=0.002, std=0.048
KS test: statistic=0.003, p-value=1.000
-> No significant difference in distribution
Synthetic shape after dropna: (6501, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    6501.000000
mean        0.002418
std         0.048371
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max         1.000000
Name: Soil_Type19, dtype: float64
No description has been provided for this image
Feature: Soil_Type20
Original: mean=0.016, std=0.126
Synthetic: mean=0.025, std=0.156
KS test: statistic=0.010, p-value=0.783
-> No significant difference in distribution
Synthetic shape after dropna: (6501, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    6501.000000
mean        0.025161
std         0.155818
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max         1.000000
Name: Soil_Type20, dtype: float64
No description has been provided for this image
Feature: Soil_Type21
Original: mean=0.000, std=0.000
Synthetic: mean=0.002, std=0.043
KS test: statistic=0.003, p-value=1.000
-> No significant difference in distribution
Synthetic shape after dropna: (6501, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    6501.000000
mean        0.002046
std         0.043267
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max         1.000000
Name: Soil_Type21, dtype: float64
No description has been provided for this image
Feature: Soil_Type22
Original: mean=0.000, std=0.000
Synthetic: mean=0.059, std=0.235
KS test: statistic=0.059, p-value=0.000
-> Significant difference detected!
Synthetic shape after dropna: (6501, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    6501.000000
mean        0.058983
std         0.235366
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max         1.000000
Name: Soil_Type22, dtype: float64
No description has been provided for this image
Feature: Soil_Type23
Original: mean=0.002, std=0.040
Synthetic: mean=0.076, std=0.264
KS test: statistic=0.075, p-value=0.000
-> Significant difference detected!
Synthetic shape after dropna: (6501, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    6501.000000
mean        0.075911
std         0.264481
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max         1.000000
Name: Soil_Type23, dtype: float64
No description has been provided for this image
Feature: Soil_Type24
Original: mean=0.008, std=0.089
Synthetic: mean=0.031, std=0.172
KS test: statistic=0.023, p-value=0.018
-> Significant difference detected!
Synthetic shape after dropna: (6501, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    6501.000000
mean        0.030809
std         0.172471
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max         1.000000
Name: Soil_Type24, dtype: float64
No description has been provided for this image
Feature: Soil_Type25
Original: mean=0.000, std=0.000
Synthetic: mean=0.001, std=0.018
KS test: statistic=0.001, p-value=1.000
-> No significant difference in distribution
Synthetic shape after dropna: (6501, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    6501.000000
mean        0.000557
std         0.018492
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max         0.780000
Name: Soil_Type25, dtype: float64
No description has been provided for this image
Feature: Soil_Type26
Original: mean=0.000, std=0.000
Synthetic: mean=0.002, std=0.047
KS test: statistic=0.003, p-value=1.000
-> No significant difference in distribution
Synthetic shape after dropna: (6501, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    6501.000000
mean        0.002323
std         0.047168
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max         1.000000
Name: Soil_Type26, dtype: float64
No description has been provided for this image
Feature: Soil_Type27
Original: mean=0.000, std=0.000
Synthetic: mean=0.002, std=0.042
KS test: statistic=0.002, p-value=1.000
-> No significant difference in distribution
Synthetic shape after dropna: (6501, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    6501.000000
mean        0.001844
std         0.041535
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max         1.000000
Name: Soil_Type27, dtype: float64
No description has been provided for this image
Feature: Soil_Type28
Original: mean=0.000, std=0.000
Synthetic: mean=0.002, std=0.038
KS test: statistic=0.002, p-value=1.000
-> No significant difference in distribution
Synthetic shape after dropna: (6501, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    6501.000000
mean        0.001541
std         0.037697
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max         1.000000
Name: Soil_Type28, dtype: float64
No description has been provided for this image
Feature: Soil_Type29
Original: mean=0.000, std=0.000
Synthetic: mean=0.182, std=0.386
KS test: statistic=0.183, p-value=0.000
-> Significant difference detected!
Synthetic shape after dropna: (6501, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    6501.000000
mean        0.182360
std         0.385972
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max         1.000000
Name: Soil_Type29, dtype: float64
No description has been provided for this image
Feature: Soil_Type30
Original: mean=0.000, std=0.000
Synthetic: mean=0.030, std=0.171
KS test: statistic=0.031, p-value=0.000
-> Significant difference detected!
Synthetic shape after dropna: (6501, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    6501.000000
mean        0.030340
std         0.170979
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max         1.000000
Name: Soil_Type30, dtype: float64
No description has been provided for this image
Feature: Soil_Type31
Original: mean=0.003, std=0.057
Synthetic: mean=0.042, std=0.201
KS test: statistic=0.039, p-value=0.000
-> Significant difference detected!
Synthetic shape after dropna: (6501, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    6501.000000
mean        0.042086
std         0.200640
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max         1.000000
Name: Soil_Type31, dtype: float64
No description has been provided for this image
Feature: Soil_Type32
Original: mean=0.012, std=0.107
Synthetic: mean=0.090, std=0.286
KS test: statistic=0.079, p-value=0.000
-> Significant difference detected!
Synthetic shape after dropna: (6501, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    6501.000000
mean        0.089780
std         0.285511
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max         1.000000
Name: Soil_Type32, dtype: float64
No description has been provided for this image
Feature: Soil_Type33
Original: mean=0.030, std=0.170
Synthetic: mean=0.059, std=0.236
KS test: statistic=0.030, p-value=0.001
-> Significant difference detected!
Synthetic shape after dropna: (6501, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    6501.000000
mean        0.059231
std         0.235971
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max         1.000000
Name: Soil_Type33, dtype: float64
No description has been provided for this image
Feature: Soil_Type34
Original: mean=0.001, std=0.026
Synthetic: mean=0.003, std=0.053
KS test: statistic=0.003, p-value=1.000
-> No significant difference in distribution
Synthetic shape after dropna: (6501, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    6501.000000
mean        0.002935
std         0.053131
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max         1.000000
Name: Soil_Type34, dtype: float64
No description has been provided for this image
Feature: Soil_Type35
Original: mean=0.000, std=0.000
Synthetic: mean=0.002, std=0.047
KS test: statistic=0.003, p-value=1.000
-> No significant difference in distribution
Synthetic shape after dropna: (6501, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    6501.000000
mean        0.002369
std         0.047255
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max         1.000000
Name: Soil_Type35, dtype: float64
No description has been provided for this image
Feature: Soil_Type36
Original: mean=0.000, std=0.000
Synthetic: mean=0.001, std=0.022
KS test: statistic=0.001, p-value=1.000
-> No significant difference in distribution
Synthetic shape after dropna: (6501, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    6501.000000
mean        0.000721
std         0.022242
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max         1.000000
Name: Soil_Type36, dtype: float64
No description has been provided for this image
Feature: Soil_Type37
Original: mean=0.000, std=0.000
Synthetic: mean=0.001, std=0.022
KS test: statistic=0.002, p-value=1.000
-> No significant difference in distribution
Synthetic shape after dropna: (6501, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    6501.000000
mean        0.000709
std         0.021770
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max         0.950000
Name: Soil_Type37, dtype: float64
No description has been provided for this image
Feature: Soil_Type38
Original: mean=0.000, std=0.000
Synthetic: mean=0.002, std=0.039
KS test: statistic=0.002, p-value=1.000
-> No significant difference in distribution
Synthetic shape after dropna: (6501, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    6501.000000
mean        0.001538
std         0.039193
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max         1.000000
Name: Soil_Type38, dtype: float64
No description has been provided for this image
Feature: Soil_Type39
Original: mean=0.000, std=0.000
Synthetic: mean=0.000, std=0.012
KS test: statistic=0.000, p-value=1.000
-> No significant difference in distribution
Synthetic shape after dropna: (6501, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    6501.000000
mean        0.000154
std         0.012403
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max         1.000000
Name: Soil_Type39, dtype: float64
No description has been provided for this image
Feature: Soil_Type40
Original: mean=0.000, std=0.000
Synthetic: mean=0.000, std=0.018
KS test: statistic=0.000, p-value=1.000
-> No significant difference in distribution
Synthetic shape after dropna: (6501, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    6501.000000
mean        0.000308
std         0.017538
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max         1.000000
Name: Soil_Type40, dtype: float64
No description has been provided for this image
### Categorical Features Validation ###

Feature: Cover_Type
Original counts:
 Cover_Type
6    13003
Name: count, dtype: int64
Synthetic counts:
 Cover_Type
6    6501
Name: count, dtype: int64
Chi-squared test: statistic=0.000, p-value=1.000
-> No significant difference in categorical distribution
No description has been provided for this image
### Coverage Metric ###
Coverage: 0.00% of original samples have a synthetic neighbor within 0.5

### Diversity Metric ###
Average pairwise distance among synthetic samples: 1816.268
Standard deviation of pairwise distances: 1051.183

### Density Metric ###
Average local density: 0.000 neighbors within a radius of 0.5

### Discriminative Score ###
Discriminative score (classifier accuracy): 0.910

### MMD Metric ###
Maximum Mean Discrepancy (MMD): 0.000


### Validation for class: 7 ###

### Continuous Features Validation ###

Feature: Elevation
Original: mean=3354.802, std=83.471
Synthetic: mean=3200.241, std=109.487
KS test: statistic=0.587, p-value=0.000
-> Significant difference detected!
Synthetic shape after dropna: (7410, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    7410.000000
mean     3200.240920
std       109.486672
min      2844.570000
25%      3119.685000
50%      3194.525000
75%      3268.857500
max      3692.000000
Name: Elevation, dtype: float64
No description has been provided for this image
Feature: Aspect
Original: mean=151.577, std=109.190
Synthetic: mean=146.506, std=109.409
KS test: statistic=0.061, p-value=0.000
-> Significant difference detected!
Synthetic shape after dropna: (7410, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    7410.000000
mean      146.506088
std       109.408588
min         0.000000
25%        53.797500
50%       117.000000
75%       239.000000
max       360.000000
Name: Aspect, dtype: float64
No description has been provided for this image
Feature: Slope
Original: mean=13.981, std=7.019
Synthetic: mean=10.867, std=6.036
KS test: statistic=0.235, p-value=0.000
-> Significant difference detected!
Synthetic shape after dropna: (7410, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    7410.000000
mean       10.866576
std         6.036425
min         0.000000
25%         6.000000
50%        10.000000
75%        14.000000
max        46.730000
Name: Slope, dtype: float64
No description has been provided for this image
Feature: Horizontal_Distance_To_Hydrology
Original: mean=339.285, std=279.110
Synthetic: mean=369.942, std=217.922
KS test: statistic=0.166, p-value=0.000
-> Significant difference detected!
Synthetic shape after dropna: (7410, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    7410.000000
mean      369.942289
std       217.921634
min         0.000000
25%       210.000000
50%       339.000000
75%       509.067500
max      1321.000000
Name: Horizontal_Distance_To_Hydrology, dtype: float64
No description has been provided for this image
Feature: Vertical_Distance_To_Hydrology
Original: mean=62.591, std=70.544
Synthetic: mean=51.534, std=55.371
KS test: statistic=0.080, p-value=0.000
-> Significant difference detected!
Synthetic shape after dropna: (7410, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    7410.000000
mean       51.534263
std        55.370670
min      -125.680000
25%        10.000000
50%        36.000000
75%        78.952500
max       375.760000
Name: Vertical_Distance_To_Hydrology, dtype: float64
No description has been provided for this image
Feature: Horizontal_Distance_To_Roadways
Original: mean=2701.735, std=1160.657
Synthetic: mean=3434.672, std=1515.750
KS test: statistic=0.226, p-value=0.000
-> Significant difference detected!
Synthetic shape after dropna: (7410, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    7410.000000
mean     3434.672281
std      1515.750009
min        96.800000
25%      2225.357500
50%      3331.655000
75%      4636.890000
max      7117.000000
Name: Horizontal_Distance_To_Roadways, dtype: float64
No description has been provided for this image
Feature: Hillshade_9am
Original: mean=217.561, std=23.051
Synthetic: mean=220.006, std=19.951
KS test: statistic=0.074, p-value=0.000
-> Significant difference detected!
Synthetic shape after dropna: (7410, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    7410.000000
mean      220.006135
std        19.950761
min       106.980000
25%       209.000000
50%       223.000000
75%       234.000000
max       254.000000
Name: Hillshade_9am, dtype: float64
No description has been provided for this image
Feature: Hillshade_Noon
Original: mean=222.442, std=18.401
Synthetic: mean=229.712, std=15.786
KS test: statistic=0.174, p-value=0.000
-> Significant difference detected!
Synthetic shape after dropna: (7410, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    7410.000000
mean      229.711789
std        15.786369
min       130.310000
25%       220.970000
50%       231.000000
75%       242.000000
max       254.000000
Name: Hillshade_Noon, dtype: float64
No description has been provided for this image
Feature: Hillshade_3pm
Original: mean=135.141, std=37.867
Synthetic: mean=143.248, std=35.027
KS test: statistic=0.077, p-value=0.000
-> Significant difference detected!
Synthetic shape after dropna: (7410, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    7410.000000
mean      143.248449
std        35.026611
min         0.000000
25%       121.000000
50%       143.000000
75%       167.000000
max       250.350000
Name: Hillshade_3pm, dtype: float64
No description has been provided for this image
Feature: Horizontal_Distance_To_Fire_Points
Original: mean=2067.645, std=1088.754
Synthetic: mean=2464.203, std=1460.786
KS test: statistic=0.124, p-value=0.000
-> Significant difference detected!
Synthetic shape after dropna: (7410, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    7410.00000
mean     2464.20327
std      1460.78609
min        26.14000
25%      1411.87250
50%      2198.19000
75%      3067.90750
max      7173.00000
Name: Horizontal_Distance_To_Fire_Points, dtype: float64
No description has been provided for this image
Feature: Wilderness_Area1
Original: mean=0.260, std=0.438
Synthetic: mean=0.606, std=0.488
KS test: statistic=0.348, p-value=0.000
-> Significant difference detected!
Synthetic shape after dropna: (7410, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    7410.000000
mean        0.606493
std         0.488343
min         0.000000
25%         0.000000
50%         1.000000
75%         1.000000
max         1.000000
Name: Wilderness_Area1, dtype: float64
No description has been provided for this image
Feature: Wilderness_Area2
Original: mean=0.114, std=0.317
Synthetic: mean=0.103, std=0.304
KS test: statistic=0.011, p-value=0.538
-> No significant difference in distribution
Synthetic shape after dropna: (7410, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    7410.000000
mean        0.103030
std         0.303553
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max         1.000000
Name: Wilderness_Area2, dtype: float64
No description has been provided for this image
Feature: Wilderness_Area3
Original: mean=0.627, std=0.484
Synthetic: mean=0.525, std=0.499
KS test: statistic=0.102, p-value=0.000
-> Significant difference detected!
Synthetic shape after dropna: (7410, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    7410.000000
mean        0.525354
std         0.499242
min         0.000000
25%         0.000000
50%         1.000000
75%         1.000000
max         1.000000
Name: Wilderness_Area3, dtype: float64
No description has been provided for this image
Feature: Wilderness_Area4
Original: mean=0.000, std=0.000
Synthetic: mean=0.000, std=0.000
KS test: statistic=0.000, p-value=1.000
-> No significant difference in distribution
Synthetic shape after dropna: (7410, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    7410.0
mean        0.0
std         0.0
min         0.0
25%         0.0
50%         0.0
75%         0.0
max         0.0
Name: Wilderness_Area4, dtype: float64
No description has been provided for this image
Feature: Soil_Type1
Original: mean=0.000, std=0.000
Synthetic: mean=0.001, std=0.034
KS test: statistic=0.001, p-value=1.000
-> No significant difference in distribution
Synthetic shape after dropna: (7410, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    7410.000000
mean        0.001177
std         0.033887
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max         1.000000
Name: Soil_Type1, dtype: float64
No description has been provided for this image
Feature: Soil_Type2
Original: mean=0.000, std=0.000
Synthetic: mean=0.005, std=0.070
KS test: statistic=0.005, p-value=0.999
-> No significant difference in distribution
Synthetic shape after dropna: (7410, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    7410.000000
mean        0.004989
std         0.070109
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max         1.000000
Name: Soil_Type2, dtype: float64
No description has been provided for this image
Feature: Soil_Type3
Original: mean=0.000, std=0.000
Synthetic: mean=0.001, std=0.026
KS test: statistic=0.001, p-value=1.000
-> No significant difference in distribution
Synthetic shape after dropna: (7410, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    7410.000000
mean        0.000789
std         0.026283
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max         1.000000
Name: Soil_Type3, dtype: float64
No description has been provided for this image
Feature: Soil_Type4
Original: mean=0.000, std=0.000
Synthetic: mean=0.009, std=0.095
KS test: statistic=0.010, p-value=0.704
-> No significant difference in distribution
Synthetic shape after dropna: (7410, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    7410.000000
mean        0.009370
std         0.095166
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max         1.000000
Name: Soil_Type4, dtype: float64
No description has been provided for this image
Feature: Soil_Type5
Original: mean=0.000, std=0.000
Synthetic: mean=0.001, std=0.027
KS test: statistic=0.001, p-value=1.000
-> No significant difference in distribution
Synthetic shape after dropna: (7410, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    7410.000000
mean        0.000792
std         0.027032
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max         1.000000
Name: Soil_Type5, dtype: float64
No description has been provided for this image
Feature: Soil_Type6
Original: mean=0.000, std=0.000
Synthetic: mean=0.009, std=0.092
KS test: statistic=0.009, p-value=0.811
-> No significant difference in distribution
Synthetic shape after dropna: (7410, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    7410.000000
mean        0.008696
std         0.092303
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max         1.000000
Name: Soil_Type6, dtype: float64
No description has been provided for this image
Feature: Soil_Type7
Original: mean=0.000, std=0.000
Synthetic: mean=0.000, std=0.011
KS test: statistic=0.001, p-value=1.000
-> No significant difference in distribution
Synthetic shape after dropna: (7410, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    7410.000000
mean        0.000233
std         0.011449
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max         0.730000
Name: Soil_Type7, dtype: float64
No description has been provided for this image
Feature: Soil_Type8
Original: mean=0.000, std=0.000
Synthetic: mean=0.000, std=0.013
KS test: statistic=0.001, p-value=1.000
-> No significant difference in distribution
Synthetic shape after dropna: (7410, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    7410.000000
mean        0.000332
std         0.013337
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max         0.780000
Name: Soil_Type8, dtype: float64
No description has been provided for this image
Feature: Soil_Type9
Original: mean=0.000, std=0.000
Synthetic: mean=0.001, std=0.022
KS test: statistic=0.001, p-value=1.000
-> No significant difference in distribution
Synthetic shape after dropna: (7410, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    7410.000000
mean        0.000561
std         0.021545
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max         1.000000
Name: Soil_Type9, dtype: float64
No description has been provided for this image
Feature: Soil_Type10
Original: mean=0.000, std=0.000
Synthetic: mean=0.000, std=0.016
KS test: statistic=0.000, p-value=1.000
-> No significant difference in distribution
Synthetic shape after dropna: (7410, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    7410.000000
mean        0.000270
std         0.016428
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max         1.000000
Name: Soil_Type10, dtype: float64
No description has been provided for this image
Feature: Soil_Type11
Original: mean=0.000, std=0.000
Synthetic: mean=0.010, std=0.099
KS test: statistic=0.010, p-value=0.656
-> No significant difference in distribution
Synthetic shape after dropna: (7410, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    7410.000000
mean        0.010105
std         0.099442
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max         1.000000
Name: Soil_Type11, dtype: float64
No description has been provided for this image
Feature: Soil_Type12
Original: mean=0.000, std=0.000
Synthetic: mean=0.041, std=0.198
KS test: statistic=0.041, p-value=0.000
-> Significant difference detected!
Synthetic shape after dropna: (7410, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    7410.000000
mean        0.040870
std         0.197724
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max         1.000000
Name: Soil_Type12, dtype: float64
No description has been provided for this image
Feature: Soil_Type13
Original: mean=0.000, std=0.000
Synthetic: mean=0.013, std=0.112
KS test: statistic=0.013, p-value=0.363
-> No significant difference in distribution
Synthetic shape after dropna: (7410, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    7410.000000
mean        0.012831
std         0.112100
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max         1.000000
Name: Soil_Type13, dtype: float64
No description has been provided for this image
Feature: Soil_Type14
Original: mean=0.000, std=0.000
Synthetic: mean=0.001, std=0.030
KS test: statistic=0.002, p-value=1.000
-> No significant difference in distribution
Synthetic shape after dropna: (7410, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    7410.000000
mean        0.001086
std         0.029580
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max         1.000000
Name: Soil_Type14, dtype: float64
No description has been provided for this image
Feature: Soil_Type15
Original: mean=0.000, std=0.000
Synthetic: mean=0.000, std=0.018
KS test: statistic=0.001, p-value=1.000
-> No significant difference in distribution
Synthetic shape after dropna: (7410, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    7410.000000
mean        0.000443
std         0.017505
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max         0.950000
Name: Soil_Type15, dtype: float64
No description has been provided for this image
Feature: Soil_Type16
Original: mean=0.000, std=0.000
Synthetic: mean=0.002, std=0.042
KS test: statistic=0.002, p-value=1.000
-> No significant difference in distribution
Synthetic shape after dropna: (7410, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    7410.000000
mean        0.001852
std         0.042118
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max         1.000000
Name: Soil_Type16, dtype: float64
No description has been provided for this image
Feature: Soil_Type17
Original: mean=0.000, std=0.000
Synthetic: mean=0.000, std=0.012
KS test: statistic=0.000, p-value=1.000
-> No significant difference in distribution
Synthetic shape after dropna: (7410, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    7410.000000
mean        0.000135
std         0.011617
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max         1.000000
Name: Soil_Type17, dtype: float64
No description has been provided for this image
Feature: Soil_Type18
Original: mean=0.000, std=0.000
Synthetic: mean=0.002, std=0.038
KS test: statistic=0.002, p-value=1.000
-> No significant difference in distribution
Synthetic shape after dropna: (7410, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    7410.000000
mean        0.001592
std         0.038201
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max         1.000000
Name: Soil_Type18, dtype: float64
No description has been provided for this image
Feature: Soil_Type19
Original: mean=0.000, std=0.014
Synthetic: mean=0.009, std=0.095
KS test: statistic=0.010, p-value=0.712
-> No significant difference in distribution
Synthetic shape after dropna: (7410, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    7410.000000
mean        0.009395
std         0.094999
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max         1.000000
Name: Soil_Type19, dtype: float64
No description has been provided for this image
Feature: Soil_Type20
Original: mean=0.000, std=0.000
Synthetic: mean=0.009, std=0.096
KS test: statistic=0.010, p-value=0.751
-> No significant difference in distribution
Synthetic shape after dropna: (7410, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    7410.000000
mean        0.009333
std         0.095783
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max         1.000000
Name: Soil_Type20, dtype: float64
No description has been provided for this image
Feature: Soil_Type21
Original: mean=0.001, std=0.023
Synthetic: mean=0.001, std=0.033
KS test: statistic=0.001, p-value=1.000
-> No significant difference in distribution
Synthetic shape after dropna: (7410, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    7410.000000
mean        0.001213
std         0.032829
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max         1.000000
Name: Soil_Type21, dtype: float64
No description has been provided for this image
Feature: Soil_Type22
Original: mean=0.007, std=0.085
Synthetic: mean=0.067, std=0.249
KS test: statistic=0.060, p-value=0.000
-> Significant difference detected!
Synthetic shape after dropna: (7410, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    7410.000000
mean        0.066723
std         0.249109
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max         1.000000
Name: Soil_Type22, dtype: float64
No description has been provided for this image
Feature: Soil_Type23
Original: mean=0.036, std=0.187
Synthetic: mean=0.115, std=0.319
KS test: statistic=0.079, p-value=0.000
-> Significant difference detected!
Synthetic shape after dropna: (7410, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    7410.000000
mean        0.115116
std         0.318970
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max         1.000000
Name: Soil_Type23, dtype: float64
No description has been provided for this image
Feature: Soil_Type24
Original: mean=0.009, std=0.096
Synthetic: mean=0.045, std=0.207
KS test: statistic=0.036, p-value=0.000
-> Significant difference detected!
Synthetic shape after dropna: (7410, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    7410.000000
mean        0.045119
std         0.207130
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max         1.000000
Name: Soil_Type24, dtype: float64
No description has been provided for this image
Feature: Soil_Type25
Original: mean=0.000, std=0.000
Synthetic: mean=0.000, std=0.019
KS test: statistic=0.001, p-value=1.000
-> No significant difference in distribution
Synthetic shape after dropna: (7410, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    7410.000000
mean        0.000468
std         0.018610
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max         1.000000
Name: Soil_Type25, dtype: float64
No description has been provided for this image
Feature: Soil_Type26
Original: mean=0.000, std=0.000
Synthetic: mean=0.007, std=0.084
KS test: statistic=0.008, p-value=0.900
-> No significant difference in distribution
Synthetic shape after dropna: (7410, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    7410.000000
mean        0.007443
std         0.084463
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max         1.000000
Name: Soil_Type26, dtype: float64
No description has been provided for this image
Feature: Soil_Type27
Original: mean=0.001, std=0.038
Synthetic: mean=0.002, std=0.038
KS test: statistic=0.001, p-value=1.000
-> No significant difference in distribution
Synthetic shape after dropna: (7410, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    7410.000000
mean        0.001727
std         0.038387
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max         1.000000
Name: Soil_Type27, dtype: float64
No description has been provided for this image
Feature: Soil_Type28
Original: mean=0.000, std=0.000
Synthetic: mean=0.002, std=0.041
KS test: statistic=0.003, p-value=1.000
-> No significant difference in distribution
Synthetic shape after dropna: (7410, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    7410.000000
mean        0.001958
std         0.041162
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max         1.000000
Name: Soil_Type28, dtype: float64
No description has been provided for this image
Feature: Soil_Type29
Original: mean=0.041, std=0.199
Synthetic: mean=0.206, std=0.404
KS test: statistic=0.165, p-value=0.000
-> Significant difference detected!
Synthetic shape after dropna: (7410, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    7410.000000
mean        0.206287
std         0.404483
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max         1.000000
Name: Soil_Type29, dtype: float64
No description has been provided for this image
Feature: Soil_Type30
Original: mean=0.008, std=0.091
Synthetic: mean=0.030, std=0.169
KS test: statistic=0.021, p-value=0.021
-> Significant difference detected!
Synthetic shape after dropna: (7410, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    7410.000000
mean        0.029505
std         0.168935
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max         1.000000
Name: Soil_Type30, dtype: float64
No description has been provided for this image
Feature: Soil_Type31
Original: mean=0.011, std=0.105
Synthetic: mean=0.045, std=0.206
KS test: statistic=0.034, p-value=0.000
-> Significant difference detected!
Synthetic shape after dropna: (7410, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    7410.000000
mean        0.044509
std         0.205662
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max         1.000000
Name: Soil_Type31, dtype: float64
No description has been provided for this image
Feature: Soil_Type32
Original: mean=0.043, std=0.203
Synthetic: mean=0.101, std=0.301
KS test: statistic=0.059, p-value=0.000
-> Significant difference detected!
Synthetic shape after dropna: (7410, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    7410.000000
mean        0.101202
std         0.301285
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max         1.000000
Name: Soil_Type32, dtype: float64
No description has been provided for this image
Feature: Soil_Type33
Original: mean=0.032, std=0.177
Synthetic: mean=0.086, std=0.279
KS test: statistic=0.054, p-value=0.000
-> Significant difference detected!
Synthetic shape after dropna: (7410, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    7410.000000
mean        0.085738
std         0.279443
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max         1.000000
Name: Soil_Type33, dtype: float64
No description has been provided for this image
Feature: Soil_Type34
Original: mean=0.002, std=0.049
Synthetic: mean=0.003, std=0.051
KS test: statistic=0.001, p-value=1.000
-> No significant difference in distribution
Synthetic shape after dropna: (7410, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    7410.000000
mean        0.002874
std         0.051249
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max         1.000000
Name: Soil_Type34, dtype: float64
No description has been provided for this image
Feature: Soil_Type35
Original: mean=0.049, std=0.215
Synthetic: mean=0.005, std=0.069
KS test: statistic=0.044, p-value=0.000
-> Significant difference detected!
Synthetic shape after dropna: (7410, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    7410.000000
mean        0.005012
std         0.069052
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max         1.000000
Name: Soil_Type35, dtype: float64
No description has been provided for this image
Feature: Soil_Type36
Original: mean=0.003, std=0.056
Synthetic: mean=0.000, std=0.013
KS test: statistic=0.003, p-value=1.000
-> No significant difference in distribution
Synthetic shape after dropna: (7410, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    7410.000000
mean        0.000246
std         0.013425
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max         0.840000
Name: Soil_Type36, dtype: float64
No description has been provided for this image
Feature: Soil_Type37
Original: mean=0.015, std=0.120
Synthetic: mean=0.001, std=0.019
KS test: statistic=0.015, p-value=0.242
-> No significant difference in distribution
Synthetic shape after dropna: (7410, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    7410.000000
mean        0.000524
std         0.019385
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max         1.000000
Name: Soil_Type37, dtype: float64
No description has been provided for this image
Feature: Soil_Type38
Original: mean=0.309, std=0.462
Synthetic: mean=0.066, std=0.248
KS test: statistic=0.244, p-value=0.000
-> Significant difference detected!
Synthetic shape after dropna: (7410, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    7410.000000
mean        0.066329
std         0.248044
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max         1.000000
Name: Soil_Type38, dtype: float64
No description has been provided for this image
Feature: Soil_Type39
Original: mean=0.273, std=0.445
Synthetic: mean=0.058, std=0.233
KS test: statistic=0.216, p-value=0.000
-> Significant difference detected!
Synthetic shape after dropna: (7410, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    7410.000000
mean        0.057900
std         0.232686
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max         1.000000
Name: Soil_Type39, dtype: float64
No description has been provided for this image
Feature: Soil_Type40
Original: mean=0.158, std=0.365
Synthetic: mean=0.033, std=0.177
KS test: statistic=0.127, p-value=0.000
-> Significant difference detected!
Synthetic shape after dropna: (7410, 56)
Any NA in mcg for synthetic? 0
Synthetic mcg describe:
 count    7410.000000
mean        0.032846
std         0.177085
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max         1.000000
Name: Soil_Type40, dtype: float64
No description has been provided for this image
### Categorical Features Validation ###

Feature: Cover_Type
Original counts:
 Cover_Type
7    14820
Name: count, dtype: int64
Synthetic counts:
 Cover_Type
7    7410
Name: count, dtype: int64
Chi-squared test: statistic=0.000, p-value=1.000
-> No significant difference in categorical distribution
No description has been provided for this image
### Coverage Metric ###
Coverage: 0.00% of original samples have a synthetic neighbor within 0.5

### Diversity Metric ###
Average pairwise distance among synthetic samples: 2668.566
Standard deviation of pairwise distances: 1376.369

### Density Metric ###
Average local density: 0.000 neighbors within a radius of 0.5

### Discriminative Score ###
Discriminative score (classifier accuracy): 0.852

### MMD Metric ###
Maximum Mean Discrepancy (MMD): 0.000
Validation metrics:
{4: {'continuous': {'Elevation': {'orig_mean': 2224.680582524272, 'orig_std': 101.53968598851593, 'synth_mean': 2552.034553398058, 'synth_std': 244.54675411155262, 'ks_stat': 0.7004854368932039, 'ks_p': 1.27e-321}, 'Aspect': {'orig_mean': 137.6, 'orig_std': 87.46085893447477, 'synth_mean': 115.96998058252426, 'synth_std': 88.48557076289768, 'ks_stat': 0.21067961165048543, 'ks_p': 4.07562087722522e-27}, 'Slope': {'orig_mean': 18.60970873786408, 'orig_std': 9.279644641618406, 'synth_mean': 17.367834951456313, 'synth_std': 6.876849762423798, 'ks_stat': 0.13980582524271845, 'ks_p': 3.9297757667819e-12}, 'Horizontal_Distance_To_Hydrology': {'orig_mean': 105.25873786407767, 'orig_std': 139.05169896237058, 'synth_mean': 94.66713592233009, 'synth_std': 94.63499622777543, 'ks_stat': 0.23058252427184467, 'ks_p': 1.9098016114115843e-32}, 'Vertical_Distance_To_Hydrology': {'orig_mean': 40.335436893203884, 'orig_std': 58.823198076201145, 'synth_mean': 22.769378640776697, 'synth_std': 36.872228147404606, 'ks_stat': 0.20242718446601943, 'ks_p': 4.724801095599635e-25}, 'Horizontal_Distance_To_Roadways': {'orig_mean': 920.2703883495145, 'orig_std': 368.14269040962176, 'synth_mean': 1128.8251359223302, 'synth_std': 817.7653983646048, 'ks_stat': 0.2262135922330097, 'ks_p': 3.1150656629825583e-31}, 'Hillshade_9am': {'orig_mean': 228.0631067961165, 'orig_std': 24.381656534870892, 'synth_mean': 224.60786407766994, 'synth_std': 17.487469897109303, 'ks_stat': 0.2436893203883495, 'ks_p': 3.153578898400587e-36}, 'Hillshade_Noon': {'orig_mean': 216.89854368932038, 'orig_std': 20.882750405835377, 'synth_mean': 220.40739805825243, 'synth_std': 19.620388084782356, 'ks_stat': 0.09271844660194174, 'ks_p': 1.4418752472206803e-05}, 'Hillshade_3pm': {'orig_mean': 111.64174757281553, 'orig_std': 49.050952335963395, 'synth_mean': 115.81266019417477, 'synth_std': 35.77751045740475, 'ks_stat': 0.1470873786407767, 'ks_p': 2.1835767343967275e-13}, 'Horizontal_Distance_To_Fire_Points': {'orig_mean': 860.6975728155339, 'orig_std': 480.8513630127819, 'synth_mean': 1193.6482912621357, 'synth_std': 773.0640617814727, 'ks_stat': 0.17184466019417477, 'ks_p': 3.861864573262583e-18}, 'Wilderness_Area1': {'orig_mean': 0.0, 'orig_std': 0.0, 'synth_mean': 0.170873786407767, 'synth_std': 0.37658149005407054, 'ks_stat': 0.170873786407767, 'ks_p': 6.128092020672713e-18}, 'Wilderness_Area2': {'orig_mean': 0.0, 'orig_std': 0.0, 'synth_mean': 0.013165048543689321, 'synth_std': 0.11298185626996733, 'ks_stat': 0.013592233009708738, 'ks_p': 0.9995362323903833}, 'Wilderness_Area3': {'orig_mean': 0.0, 'orig_std': 0.0, 'synth_mean': 0.027184466019417475, 'synth_std': 0.16269963427331025, 'ks_stat': 0.027184466019417475, 'ks_p': 0.6872238461926288}, 'Wilderness_Area4': {'orig_mean': 1.0, 'orig_std': 0.0, 'synth_mean': 0.5249611650485437, 'synth_std': 0.49877941244649265, 'ks_stat': 0.4766990291262136, 'ks_p': 7.498352025534808e-142}, 'Soil_Type1': {'orig_mean': 0.06553398058252427, 'orig_std': 0.2475257970684189, 'synth_mean': 0.03681553398058253, 'synth_std': 0.1866349626470626, 'ks_stat': 0.032524271844660196, 'ks_p': 0.4587998035108972}, 'Soil_Type2': {'orig_mean': 0.04466019417475728, 'orig_std': 0.20660683138770405, 'synth_mean': 0.017572815533980584, 'synth_std': 0.12970450096016153, 'ks_stat': 0.02815533980582524, 'ks_p': 0.6443546163811467}, 'Soil_Type3': {'orig_mean': 0.3674757281553398, 'orig_std': 0.4822345962511095, 'synth_mean': 0.11440776699029126, 'synth_std': 0.31587136908163177, 'ks_stat': 0.25679611650485434, 'ks_p': 3.1501157914029972e-40}, 'Soil_Type4': {'orig_mean': 0.062135922330097085, 'orig_std': 0.2414608707107312, 'synth_mean': 0.038427184466019414, 'synth_std': 0.19148301601345302, 'ks_stat': 0.02524271844660194, 'ks_p': 0.7708717232475797}, 'Soil_Type5': {'orig_mean': 0.018932038834951457, 'orig_std': 0.13631814786846588, 'synth_mean': 0.014825242718446601, 'synth_std': 0.11599872047688023, 'ks_stat': 0.006310679611650485, 'ks_p': 1.0}, 'Soil_Type6': {'orig_mean': 0.11407766990291263, 'orig_std': 0.3179827654638115, 'synth_mean': 0.022611650485436893, 'synth_std': 0.14735067109551128, 'ks_stat': 0.09271844660194174, 'ks_p': 1.4418752472206803e-05}, 'Soil_Type7': {'orig_mean': 0.0, 'orig_std': 0.0, 'synth_mean': 0.001912621359223301, 'synth_std': 0.03779814599350717, 'ks_stat': 0.002912621359223301, 'ks_p': 1.0}, 'Soil_Type8': {'orig_mean': 0.0, 'orig_std': 0.0, 'synth_mean': 0.0016213592233009708, 'synth_std': 0.03748912806569954, 'ks_stat': 0.001941747572815534, 'ks_p': 1.0}, 'Soil_Type9': {'orig_mean': 0.0, 'orig_std': 0.0, 'synth_mean': 0.011699029126213593, 'synth_std': 0.10600954675378889, 'ks_stat': 0.013592233009708738, 'ks_p': 0.9995362323903833}, 'Soil_Type10': {'orig_mean': 0.08009708737864078, 'orig_std': 0.27150935358345324, 'synth_mean': 0.005825242718446602, 'synth_std': 0.07613762118849192, 'ks_stat': 0.07427184466019418, 'ks_p': 0.0010041538761779232}, 'Soil_Type11': {'orig_mean': 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'Soil_Type40': {'orig_mean': 0.0, 'orig_std': 0.0, 'synth_mean': 0.0, 'synth_std': 0.0, 'ks_stat': 0.0, 'ks_p': 1.0}}, 'categorical': {'Cover_Type': {'orig_counts': {4: 2060}, 'synth_counts': {4: 1030}, 'chi2_stat': 0.0, 'chi2_p': 1.0}}, 'coverage': 0.0, 'diversity': {'avg_distance': 1412.6391380134398, 'std_distance': 834.9383754217445}, 'density': {'density_threshold': 0.5, 'average_density': 0.0, 'neighbor_counts': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]}, 'discriminative_score': 0.8524516419253262, 'mmd': 0.00020242914979757084}}

### Target Distribution Analysis on Original Training Set ###
Target counts:
Cover_Type
2    208214
1    155577
3     26803
7     14820
6     13003
5      6808
4      2060
Name: count, dtype: int64

Relative frequencies:
Cover_Type
2    0.49
1    0.36
3    0.06
7    0.03
6    0.03
5    0.02
4    0.00
Name: count, dtype: float64
No description has been provided for this image
### Target Distribution Analysis on Augmented Training Set ###
Target counts:
Cover_Type
2    208214
1    155577
3     26803
7     22230
6     19504
5     10212
4      3090
Name: count, dtype: int64

Relative frequencies:
Cover_Type
2    0.47
1    0.35
3    0.06
7    0.05
6    0.04
5    0.02
4    0.01
Name: count, dtype: float64
No description has been provided for this image
### Target Distribution Analysis on Test Set ###
Target counts:
Cover_Type
2    69405
1    51859
3     8935
7     4940
6     4335
5     2269
4      686
Name: count, dtype: int64

Relative frequencies:
Cover_Type
2    0.49
1    0.36
3    0.06
7    0.03
6    0.03
5    0.02
4    0.00
Name: count, dtype: float64
No description has been provided for this image
In [5]:
import pandas as pd
from MachineLearningModels.MultiClassLogisticRegression import train_logistic_model, evaluate_model, compare_models
from sklearn.preprocessing import LabelEncoder
import numpy as np

# Load CSV files for the Yeast dataset.
original_train = pd.read_csv("OutputTrainingSets/original_trainVAEForestFINAL.csv")
augmented_train = pd.read_csv("OutputTrainingSets/augmented_trainVAEForestFINAL.csv")
test_set = pd.read_csv("OutputTrainingSets/test_setVAEForestFINAL.csv")

# Define the feature columns and target.
features = numeric_features
#          OR
#features = continuous_features

categorical_features = ["Cover_Type"]

# Re-map the target column so that labels are contiguous (0, 1, 2, ...).
le = LabelEncoder()
original_train[target] = le.fit_transform(original_train[target])
augmented_train[target] = le.transform(augmented_train[target])
test_set[target] = le.transform(test_set[target])

# Train a Logistic Regression model on the original training dataset.
model_original = train_logistic_model(original_train, features, target)
metrics_original = evaluate_model(model_original, test_set, features, target)

# Train a Logistic Regression model on the augmented training dataset.
model_augmented = train_logistic_model(augmented_train, features, target)
metrics_augmented = evaluate_model(model_augmented, test_set, features, target)

unique_labels = np.sort(metrics_original['y_test'].unique())
# Now, inverse transform these labels using the same LabelEncoder 'le' used earlier
target_names = [str(x) for x in le.inverse_transform(unique_labels)]

# Compare the performance of the two models.
compare_models(metrics_original, metrics_augmented, target_names)
=== Original Training Model Metrics ===
Accuracy: 0.720
AUC: 0.930
Classification Report:
              precision    recall  f1-score   support

           1       0.72      0.74      0.73     51859
           2       0.73      0.80      0.76     69405
           3       0.61      0.65      0.63      8935
           4       0.43      0.34      0.38       686
           5       0.67      0.02      0.04      2269
           6       0.42      0.12      0.18      4335
           7       0.97      0.37      0.54      4940

    accuracy                           0.72    142429
   macro avg       0.65      0.43      0.47    142429
weighted avg       0.72      0.72      0.71    142429

Confusion Matrix:
[[38574 13257     8     0     0     0    20]
 [12426 55499  1336     0    21   106    17]
 [    1  2263  5806   258     0   607     0]
 [    0    20   433   231     0     2     0]
 [    1  2159    54     0    42     0    13]
 [    1  1841  1929    48     0   514     2]
 [ 2376   730     0     0     0     0  1834]]

=== Augmented Training Model Metrics ===
Accuracy: 0.736
AUC: 0.937
Classification Report:
              precision    recall  f1-score   support

           1       0.74      0.74      0.74     51859
           2       0.74      0.78      0.76     69405
           3       0.64      0.60      0.62      8935
           4       0.38      0.52      0.44       686
           5       1.00      0.00      0.00      2269
           6       0.52      0.39      0.45      4335
           7       1.00      0.92      0.96      4940

    accuracy                           0.74    142429
   macro avg       0.72      0.56      0.57    142429
weighted avg       0.74      0.74      0.73    142429

Confusion Matrix:
[[38380 13457     9    12     0     0     1]
 [13401 54442  1283    13     0   253    13]
 [    1  1890  5344   458     0  1242     0]
 [    0     5   294   356     0    31     0]
 [    6  2190    61     0     2     9     1]
 [    0  1143  1396   106     0  1687     3]
 [   63   330     0     0     0     0  4547]]
In [ ]:

In [ ]:
 
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In [ ]:
"""
import pandas as pd
from MachineLearningModels.MultiClassXGBoostComparison import train_xgb_model, evaluate_model, compare_models
from sklearn.preprocessing import LabelEncoder
import numpy as np

# Load CSV files for the Yeast dataset.
original_train = pd.read_csv("OutputTrainingSets/original_trainVAEForest2.csv")
augmented_train = pd.read_csv("OutputTrainingSets/augmented_trainVAEForest2.csv")
test_set = pd.read_csv("OutputTrainingSets/test_setVAEForest2.csv")
    
# Define the feature columns and target.
# For the Yeast dataset, typical numeric features are:
features = numeric_features
categorical_features = ["Cover_Type"]

# Re-map the target column so that labels are contiguous integers (0, 1, 2, …)
le = LabelEncoder()
original_train[target] = le.fit_transform(original_train[target])
augmented_train[target] = le.transform(augmented_train[target])
test_set[target] = le.transform(test_set[target])
    
# Train a multi-class XGBoost model on the original training dataset.
model_original = train_xgb_model(original_train, features, target)
metrics_original = evaluate_model(model_original, test_set, features, target)
    
# Train a multi-class XGBoost model on the augmented training dataset.
model_augmented = train_xgb_model(augmented_train, features, target)
metrics_augmented = evaluate_model(model_augmented, test_set, features, target)

unique_labels = np.sort(metrics_original['y_test'].unique())
# Now, inverse transform these labels using the same LabelEncoder 'le' used earlier
target_names = [str(x) for x in le.inverse_transform(unique_labels)]

# Compare the performance of the two models.
compare_models(metrics_original, metrics_augmented, target_names)
"""
In [ ]:
 
In [ ]:
"""
import pandas as pd
from MachineLearningModels.MultiClassRandomForest import train_rf_model, evaluate_model, compare_models
from sklearn.preprocessing import LabelEncoder
import numpy as np

# Load CSV files for the Yeast dataset.
original_train = pd.read_csv("OutputTrainingSets/original_trainVAEForest.csv")
augmented_train = pd.read_csv("OutputTrainingSets/augmented_trainVAEForest.csv")
test_set = pd.read_csv("OutputTrainingSets/test_setVAEForest.csv")

# Define the feature columns and target.
features = numeric_features
categorical_features = ["Cover_Type"]

# Re-map the target column so that labels are contiguous.
le = LabelEncoder()
original_train[target] = le.fit_transform(original_train[target])
augmented_train[target] = le.transform(augmented_train[target])
test_set[target] = le.transform(test_set[target])

# Train a Random Forest model on the original training dataset.
model_original = train_rf_model(original_train, features, target)
metrics_original = evaluate_model(model_original, test_set, features, target)

# Train a Random Forest model on the augmented training dataset.
model_augmented = train_rf_model(augmented_train, features, target)
metrics_augmented = evaluate_model(model_augmented, test_set, features, target)

unique_labels = np.sort(metrics_original['y_test'].unique())
# Now, inverse transform these labels using the same LabelEncoder 'le' used earlier
target_names = [str(x) for x in le.inverse_transform(unique_labels)]

# Compare the performance of the two models.
compare_models(metrics_original, metrics_augmented, target_names)
"""
In [ ]:
"""
import pandas as pd
from MachineLearningModels.MultiClassKNN import train_knn_model, evaluate_model, compare_models
from sklearn.preprocessing import LabelEncoder
import numpy as np

# Load CSV files for the Yeast dataset.
original_train = pd.read_csv("OutputTrainingSets/original_trainVAEForest.csv")
augmented_train = pd.read_csv("OutputTrainingSets/augmented_trainVAEForest.csv")
test_set = pd.read_csv("OutputTrainingSets/test_setVAEForest.csv")

# Define the feature columns and target.
features = numeric_features
categorical_features = ["Cover_Type"]

# Re-map the target column so that labels are contiguous.
le = LabelEncoder()
original_train[target] = le.fit_transform(original_train[target])
augmented_train[target] = le.transform(augmented_train[target])
test_set[target] = le.transform(test_set[target])

# Train a KNN model on the original training dataset.
model_original = train_knn_model(original_train, features, target)
metrics_original = evaluate_model(model_original, test_set, features, target)

# Train a KNN model on the augmented training dataset.
model_augmented = train_knn_model(augmented_train, features, target)
metrics_augmented = evaluate_model(model_augmented, test_set, features, target)

unique_labels = np.sort(metrics_original['y_test'].unique())
# Now, inverse transform these labels using the same LabelEncoder 'le' used earlier
target_names = [str(x) for x in le.inverse_transform(unique_labels)]

# Compare the performance of the two models.
compare_models(metrics_original, metrics_augmented, target_names)
"""
In [ ]:
 
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In [ ]:
 
In [ ]: